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31 January 2009

Prediksi Puncak Tinggi Muka Air Sungai Sunter (Prediction of Peak Water Level of Sunter River)


Prediksi puncak tinggi muka air sungai Sunter pada tahun 2009 juga akan terjadi pada bulan Februari.

The peak water level of Sunter River in 2009 will be in February.

30 January 2009

Membuang Limbah ke Sungai Ciliwung

Kompas, Selasa, 08 April 2003

Yang banyak membuat kali kotor ternyata adalah perusahaan-perusahaan besar, yaitu perusahaan trem listrik dan perusahaan gas. Menurut Bintang Betawi, "banyak trem listrik membuang minyak di kali." Sementara perusahaan gas yang terletak di Ketapang itu juga membikin kali kotor dengan membuang ter, "hingga orang-orang kampung tidak bisa cuci pakaian, apalagi mandi di kali."

Tahun lalu melalui suratkabar Batavia Nieuwsblad, seorang pembesar gubernemen mengeluarkan perintah keras, siapa pun dilarang membuang di kali segala kotoran, apalagi pada waktu air kering.

Sejumlah warga kota berkomentar, sebenarnya perintah itu tidak perlu dikeluarkan lagi, karena membuang kotoran di kali memang dilarang dalam politie reglement. Bahkan di dalam peraturan itu juga disebutkan hukuman yang diberikan. Tetapi menurut Bintang Betawi gubernemen bertindak tidak adil, karena telah melanggar sendiri ketentuan yang dibuatnya itu. Untuk membuktikan hal itu, Bintang Betawi mengajukan pertanyaan: Kotoran dari rumah sakit di Weltevreden dan Stadtverband dan dari rumah bui di mana dibuang?

Instansi-instansi yang terletak di tepi kali Ciliwung itu membuang limbahnya langsung ke kali Ciliwung. Bintang Betawi menyaksikan sendiri perbuatan pencemaran lingkungan yang sangat berat: "Hampir saban malam Minggu, kita lihat sendiri beberapa orang rante bawa tahang buang kotoran di dalam kali. Kenapa itu tiada dilarang?" (Adit SH, sejarawan dan pengamat sosial, tinggal di Jakarta)

Komentar:

Apakah penegakan hukum tidak bisa dilakukan dengan tegas ?

(HouwLiong)

29 January 2009

Mechanisms of Extreme Climate in Pontianak and Jayapura Regions

Mechanisms of Extreme Climate in Pontianak and Jayapura Regions

Plato M.Siregar1) and The Houw Liong2)

1) Science Atmosphere Division, Faculty of Earth Science and Mineral Technology, ITB
2) Physics of Complex System Division, Faculty of Mathematics and Natural Sciences, ITB

Abstract

Base on our study extreme climate in Pontianak mainly influenced by Sunspot numbers cycles and Jayapura regions are influenced by El Nino Southern Oscillation (ENSO) cycles.
In the middle of Indonesian archipelago (Pontianak Regions) heavy rainfalls are mainly local convection process, on the other hand in the eastern of Indonesian archipelago(Jayapura regions) heavy rainfalls by local convection, Walker cycles and warm pools oscillation.
The detail mechanisms are shown by Navier Stokes equation, energy conservation of mass, Clasius Clyperon, equation of state and cloud microphysics.
In this study we intend to explain the mechanism phenomenologicaly using assimilation data of precipitation, wind surface, and precipitable water.

Keyword: Solar activity, ENSO, mechanism, Navier Stokes, and extreme climate.

Background

When solar activity is low or sunspot minimum SMin, the intensity of the cosmic ray becomes maximized so that coverage of clouds grows. This means that solar radiation coming to earth will be minimized. Reversely, when solar activity is maximum ME the intensity of cosmic ray reaching lower levels of the atmosphere is minimum, the cloud covers decreases, additionally extra energy received from flares during prominent eruptions, maximized the amount of solar energy received on earth.
The global cloud cover produced global warming (the greenhouse effect) which amounts to 13%, but it also caused a cooling effect as much as 20% due to reflections against direct solar radiation1). The total energy derived from the sun is thus the solar constant averaging to 6.3 10^20 joules/hour which is equal to the energy of 40 tropical cyclones or 60 times the energy released by a major earthquake in Indonesia.
From the 21st solar cycle the irradiance received on earth shifted between 1367.0 W/m2 and 1368,5 W/m2 , it varies as much as 0,15 % or only 5%), however considering the large quantity of energy derived from the sun and due to the forcing of atmospheric dynamics and oceans the variation of the irradiance contribute considerably to the weather and climate.
Landscheidt4) has shown that between years 1950 to 1975 very strong correlations exist between the events of El Nino to sunspot minimum SMin and its harmonics to SM/2 or SMax. The occurrences of La Nina correspond to maximum eruption ME and its harmonics ME/2. Then around year 1975 a phase reversal occurred, and this happens from year 1976 up to the present, there the ME and its harmonics correlated well to El Nino, while SM and its harmonics correspond to La Nina.

Evaluasi Prediksi Banjir Sungai Ciliwung dengan ANFIS

EVALUASI PREDIKSI BANJIR DAS CILIWUNG WILAYAH JAKARTA DENGAN ANFIS
The Houw Liong1)
Acep Purqon1)
Bajong Tj.HK2)
Heru Widodo3)
1) KK Fisika Sistem klompleks, FMIPA, ITB
2) KK Sains Atmosfer , FITB , ITB
3) UPT Hujan Buatan,BPPT.
Abstrak
Faktor utama banjir DAS ( daerah aliran sungai) Ciliwung wilayah Jakarta ialah hujan lebat yang berlangsung berjam-jam untuk daerah yang cukup luas dan ditambah dengan banjir kiriman yang di bawa oleh sungai itu yang melewati Jakarta
Dalam makalah dibahas prediksi hujan lebat dan tinggi muka air sungai Ciliwung dengan menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) pada awal tahun 2003, kemudian dilakukan evaluasi metoda yang dikembangkan.
Deret waktu bilangan sunspot dapat dipakai untuk prediksi jangka sangat panjang yaitu sekitar 4 atau 5 tahun sebelum kejadian, kemudian prediksi jangka menengah beberapa bulan sebelum kejadian dilakukan dengan memakai deret waktu hujan bulanan rata-rata dari tujuh stasiun di DKI dan tinggi muka air sungai Ciliwung di Depok dan prediksi jangka pendek beberapa minggu sebelum kejadian dilakukan berdasarkan deret waktu pentadnya.

27 January 2009

Prediksi dan Peringatan Dini Banjir

Kompas, Rabu, 21 Januari 2009 | 01:55 WIB

Oleh YUNI IKAWATI

Sistem ANFIS
Selain Departemen Pekerjaan Umum dan Pemerintah Provinsi DKI Jakarta, banjir di Jakarta juga menjadi perhatian peneliti dari Institut Teknologi Bandung dan Badan Pengkajian dan Penerapan Teknologi (BPPT). Mereka juga mengembangkan sistem kecerdasan buatan yang disebut ANFIS (adaptive neuro- based fuzzy inference system), yang pertama kali diperkenalkan di Amerika oleh Jyh-Shing Roger Jang dari Departemen Teknik Listrik dan Ilmu Komputer di Universitas California.

”Perhitungan ANFIS untuk prediksi banjir di Sungai Ciliwung menggunakan data aktivitas matahari atau sunspot, historis curah hujan di Jakarta selama 50 tahun lalu, dan TMA dalam 40 tahun terakhir,” kata F Heru Widodo, Koordinator Prediksi Banjir dengan ANFIS di BPPT.

Penelitian pada tahap kedua bertujuan untuk mengembangkan sistem pemodelan dinamis, yaitu dengan menambah parameter lain, yaitu suplai air dari hulu sungai dan pasang air laut, serta tingkat penguapan air. Selain itu, diperlukan data historis yang runtut sehingga dapat diketahui lamanya genangan air di suatu tempat.

Model dinamis ini diharapkan dapat diterapkan pada tahun 2011, kata Heru yang juga Peneliti Madya di Unit Pelaksana Teknis Hujan Buatan BPPT.

Heru yang juga salah satu Kepala Program Global Warning di BPPT menjelaskan, hasil prediksi curah hujan tahun 2009 menggunakan ANFIS menunjukkan bahwa puncak curah hujan di wilayah Jakarta akan terjadi pada Februari 2009. ”Tingkat curah hujan bulanan tahun ini masih di bawah curah hujan pada tahun 2002 dan 2007 sehingga banjir yang terjadi di wilayah DKI Jakarta tahun ini tidak sebesar banjir tahun 2002 dan atau 2007,” ujarnya menjelaskan.

Adapun prediksi bilangan sunspot menunjukkan bahwa nilainya mengalami kenaikan dan mencapai puncaknya pada tahun 2012 yang diikuti oleh meningkatnya curah hujan di wilayah Jakarta.

Penerapan ANFIS ini, lanjutnya, memungkinkan upaya antisipasi dan mitigasi menghadapi banjir di DKI Jakarta hingga ke pencegahan bencana tersebut. Sekarang ini sistem yang dipasang untuk tujuan responsif berupa pemantauan untuk peringatan dini banjir.

Untuk mengembangkan sistem prediksi hingga pemantauan banjir di Jabodetabek, sistem ANFIS akan dipadukan dengan sistem pemantau radar cuaca yang dibangun di Pusat Penelitian Ilmu Pengetahuan dan Teknologi (Puspiptek) di Serpong yang juga dikelola BPPT. ”Ke depan diperlukan integrasi seluruh sistem yang dikembangkan masing-masing instansi, antara lain BMKG dan Departemen Pekerjaan Umum,” harapnya.

Sistem yang ANFIS yang dikembangkan sejak tahun 2004 bisa melakukan prediksi tahunan menjadi lima harian, bahkan hingga hitungan beberapa jam ke depan. Jauh lebih akurat!
Catatan:
Ternyata puncak sunspot terjadi pada tahun 2013, sehingga banjir besar Jakarta terjadi pada tahun 2013.
HouwLiong

ANFIS




Adaptive network-based fuzzy inference system used a feed forward network to search for fuzzy decision rules that perform well on a given task. Using a given input-output data set ANFIS creates a fuzzy inference system whose membership function parameters are adjusted using a backpropagation algorithm alone or combination between a backpropagation algorithm with a least squares method. This allows the fuzzy systems to learn from the data being modeled. ANFIS provide a method for the fuzzy modeling procedure to learn information from the data set, followed by creating the membership function parameters that best performing the given task. Consider a first order Takagi-Sugeno fuzzy model with a two input, one output system having two membership functions for each input.
The functioning of ANFIS is a five layered feed forward neural structure and the functionality of the nodes in these layers can be summarized as follows:
Layer 1: Every node i in this layer is an adaptive node with a node output defined by:


Picture: Equation(1)

where x(or y) is the input to the node; Ai (or Bi-2) is a fuzzy set associated with this node, characterized by the shape of the membership function in this node and can be any appropriate functions that are continuous and piecewise differentiable such as Gaussian , generalized bell shaped, trapezoidal shaped and triangular shaped functions. Assuming a bell shaped function as the membership function, Ai can be computed as,


Picture: Equation (2)



ai and ci are the parameter set. Parameters in this layer are referred to as premise (antecedent) parameters.
Layer 2: Every node in this layer is a fixed node labeled Π , which multiplies the incoming signals and outputs the product. For instance,

Picture: Equation(3)


Each node output represents the firing strength of a rule.
Layer 3: Every node in this layer is a circle node labeled N. The ith node calculates the ratio of the ith rule's firing strength to the sum of all rule's firing strengths. Output of this layer will be called normalized firing strengths.


Picture: Equation(4)

Layer 4: Node i in this layer compute the contribution of the ith rule towards the model output, with the following node functions:

Picture: Equation(5)

Layer 5: The single node in this layer is a fixed node labeled that computes the overall output as the summation of all incoming signals.
Overall output =

Picture : Equation (6)

The parameters are adjusted using a learning rule :

delta(p_i) = - (eta) d(e_T)/d(p_i) , i = 1, 2, 3, ....... ; /eta/ < 1,
e_T = sum square error

and data learning.

26 January 2009

Prediksi banjir dengan ANFIS


Jaringan Neural Artifisial (Artificial Neural Network) dan Logika Samar (Fuzzy Logic) atau ANFIS untuk Prediksi dan Pengenalan Pola.

The Houw Liong
KK Fisika Sistem Kompleks, FMIPA, ITB
R.Gernowo
KK Sains Atmosfer, FITB, ITB
F. H. Widodo
UPT Hujan Buatan, BPPT

Abstrak
Jaringan neural artifisial dan/atau logika samar dapat dipakai untuk melakukan tugas yang sulit dilakukan oleh komputer biasa, misalnya untuk belajar mengenal bentuk geometri, huruf tulis, suara, pola, deret waktu dll.
Dalam bab ini akan dibahas pegertian jaringan neural artifisial, neuron artifisial, adaptive neuro-fuzzy inference system (ANFIS) dan cara kerjanya dan pemakaiannya dalam prediksi dan pengenalan pola.
Konsep jaringan neural artifisial timbul dan diilhami oleh jaringan neural dalam benak manusia yang memiliki arsitektur yang sangat berlainan dengan komputer biasa. Orang menyadari bahwa membuat perangkat lunak supaya komputer memiliki kemampuan belajar seperti manusia sangat sukar. Rosenblatt berpikir bahwa kesukaran ini mungkin dapat diatasi dengan membuat komputer dengan arsitektur yang mirip dengan jaringan neural yang ada dalam benak manusia. Ilmuwan menyambut konsep ini dan banyak yang mulai ikut serta untuk mengembangkannya, sehingga sekitar tahun 1980 orang sudah mulai melihat hasilnya.
Salah satu jaringan neural artifisial yang terdiri dari tiga lapis neuron, yaitu lapisan masukan, lapisan tersembunyi dan lapisan keluaran yang bekerja memakai kaidah propagasi balik serta berbagai modifikasinya, demikian juga adaptive neuro fuzzy inference system ternyata mempunyai kemampuan belajar mengenal pola gambar ,suara, dan simbol, sehingga terbuka kemungkinan untuk memanfaatkannya dalam geofisika dan meteorologi misalnya dalam mengenal pola citra yang diambil dari satelit , klasifikasi awan, menganalisa deret waktu suhu rata-rata bumi, , deret waktu anomali suhu permukaan laut, prediksi cuaca/iklim, dll.
JNA , misalnya jenis ART dan Kohonen mampu melakukan klasifikasi demikian juga metoda yang dikembangkan dalam logika samar , dapat melakukan klasifikasi samar.
Sebagai contoh penerapan, pengaruh El Nino yang tercermin dalam anomali SST Nino 3.4 dan index SOI dapat dibahas korelasinya dengan persentase daerah kering di Indonesia dengan dengan cara statistik dan dapat dikembangkan lebih lanjut dengan memakai JNA/ logika samar seperti yang diuraikan di atas. Demikian juga pendaerahan kekeringan di Indonesia dilakukan dengan melihat garis isohyet dan index kekeringan, selanjutnya dapat diteruskan dengan memanfaatkan JNA/ logika samar untuk pengelompokanya.ANFIS dapat dipakai untuk melakukan prediksi bilangan sunspot, curah hujan bulanan dan tinggi muka air sungai.

Catatan : Tahun 2009 ANFIS memprediksi puncak hujan Jakarta ialah bulan Februari dan puncak tinggi muka air ciliwung juga terjadi pada bulan Februari sehingga diprediksi terjadi banjir dengan genangan berlangsung sekitar 2 hari.

Kata Kunci : Jaringan Neural Artifisial, Neural Network, logika samar, fuzzy logic

25 January 2009

Clues to End of the Last Ice Age

by Terah U. DeJong
Lead author Lowell Stott, a professor of earth sciences at USC College

In contrast to what is often inferred from the geologic record, carbon dioxide did not cause the end of the last ice age, a new USC study published in Science suggests.

“There has been this continual reference to the correspondence between CO2 and climate change as reflected in ice core records as justification for the role of CO2 in climate change,” said paleoclimatologist Lowell Stott, the study’s lead author and a professor of earth sciences at USC College.

“You can no longer argue that CO2 alone caused the end of the ice ages.”

Deep-sea temperatures warmed about 1,300 years before the tropical surface ocean and well before the rise in atmospheric CO2, the study found. The finding suggests the rise in greenhouse gas was likely a result of warming – but not its main cause.

However, the study does not question the fact that CO2 plays a key role in climate.

“I don’t want anyone to leave thinking that this is evidence that CO2 doesn’t affect climate,” Stott cautioned. “It does, but the important point is that CO2 is not the beginning and end of climate change.”

While an increase in atmospheric CO2 and the end of the ice ages occurred at roughly the same time, scientists have debated whether CO2 caused the warming or was released later by an already warming sea.

The best estimate from other studies of when CO2 began to rise is no earlier than 18,000 years ago. Yet this study shows that the deep sea, which reflects a good picture of oceanic temperature trends, started warming about 19,000 years ago.

“What this means is that a lot of energy went into the ocean long before the rise in atmospheric CO2,” Stott said.

But where did this energy come from? Evidence pointed southward.
Water’s salinity and temperature are properties that can be used to trace its origin – and the warming deep water appeared to come from the Antarctic Ocean, the scientists wrote.

This water then was transported northward over 1,000 years via well-known deep-sea currents, a conclusion supported by carbon-dating evidence.

In addition, the researchers noted that the increases in deep-sea temperature coincided with the retreat of Antarctic sea ice, both occurring 19,000 years ago, before the northern hemisphere’s ice retreat began.

Finally, Stott and colleagues found a correlation between melting Antarctic sea ice and increased springtime solar radiation over Antarctica, suggesting this was the energy source.

As the sun pumped in heat, the warming accelerated because of sea-ice albedo feedbacks, in which retreating ice exposes more of the ocean that can absorb heat from the sun, much like a dark T-shirt on a hot day, and this results in more melting.

In addition, the authors’ model showed how changed ocean conditions may have been responsible for the release of CO2 from the ocean into the atmosphere, which like the albedo feedbacks, also accelerated the warming.

The link between the sun and ice age cycles is not new. The theory of Milankovitch cycles states that periodic changes in Earth’s orbit cause increased summertime solar radiation in the northern hemisphere, which controls ice size.

Solusi Banjir Jakarta

Kompas, 3 Februari 2007

Solusi di hulu harus berkesinambungan, antara pembatasan penggunaan lahan, reboisasi intensif, dan pembangunan bendungan. Jika hanya satu langkah yang dilaksanakan, langkah lain akan menjadi kurang efektif.

Di hilir, selain pembuatan Banjir Kanal Timur, Firdaus mengusulkan pembuatan penampungan air bawah tanah dalam skala besar atau deep tunnel reservoir. Penampungan air bawah tanah, seperti yang diterapkan Chicago (Amerika Serikat) dan Singapura mampu menampung sekitar 200 juta meter kubik air dan dapat bertahan 125 tahun.

Ide penampungan air bawah tanah adalah menampung semua limpahan air banjir dan limbah cair dari sanitasi lingkungan ke dalam bendungan bawah tanah. Air tampungan itu dapat diolah dan digunakan sebagai cadangan air baku bagi Jakarta.

Saat ini, kata Firdaus, Indonesia menghadapi perubahan iklim akibat pemanasan global. Perubahan iklim tersebut menyebabkan musim hujan lebih pendek, tetapi curah hujan lebih tinggi.

Jika air tersebut tidak disimpan dalam penampungan yang besar, Jakarta akan terancam kekeringan dan banjir dalam waktu yang bergantian sepanjang tahun. Bencana yang akan semakin memiskinkan Indonesia.

Biaya pembuatan penampungan air bawah tanah itu, menurut Firdaus, diperkirakan "hanya" memerlukan Rp 12 triliun. Jumlah tersebut masih terjangkau oleh APBD DKI Jakarta 2007 yang mencapai Rp 21,5 triliun.

(Emilius Caesar Alexey)


Komentar:
Ada solusi yang lebih murah yaitu dengan membangun kolam penampungan atau resapan air hujan pada setiap kompleks perumahan atau RW serta normalisasi sungai dengan penghijauan daerah hulu dan sepanjang sungai yang dilakukan dengan semangat gotong royong masyarakat.

(HouwLiong)

24 January 2009

The accuracy of climate models predictions



1.According to E. Lorenz a chaotic system cannot be predicted accurately because the result of prediction is very sensitive to initial conditions (butterfly effect).

2.It is difficult to represent the natural forcings (solar activity, galactic cosmic ray flux, volcanic eruptions) in a climate model.

3. The accuracy of climate models to predict sea surface temperature anomalies in Pacific ocean 9 month ahead of the happening are only 70% (errors are about 30%).

So, we can conclude that long range predictions of climate models have low accuracies.

The chorus of skeptical scientific voices grow louder in 2008 as a steady stream of peer-reviewed studies, analyses, real world data and inconvenient developments challenged the UN’s and former Vice President Al Gore’s claims that the “science is settled” and there is a “consensus.”

On a range of issues, 2008 proved to be challenging for the promoters of man-made climate fears. Promoters of anthropogenic warming fears endured the following: Global temperatures failing to warm; Peer-reviewed studies predicting a continued lack of warming; a failed attempt to revive the discredited “Hockey Stick“; inconvenient developments and studies regarding rising CO2; the Spotless Sun; Clouds; Antarctica; the Arctic; Greenland’s ice; Mount Kilimanjaro; Global sea ice; Causes of Hurricanes; Extreme Storms; Extinctions; Floods; Droughts; Ocean Acidification; Polar Bears; Extreme weather deaths; Frogs; lack of atmospheric dust; Malaria; the failure of oceans to warm and rise as predicted.

http://eapsweb.mit.edu/research/Lorenz/Three_approaches_1969.pdf

http://wattsupwiththat.com/2009/01/27/james-hansens-former-nasa-supervisor-declares-himself-a-skeptic-says-hansen-embarrassed-nasa-was-never-muzzled/

23 January 2009

Correlation between cosmic rays and temperature of the stratosphere


What did surprise the scientists, however, were the intermittent and sudden increases observed in the levels of muons during the winter months. These jumps in the data occurred over just a few days. On investigation, they found these changes coincided with very sudden increases in the temperature of the stratosphere (by up to 40 oC in places!). Looking more closely at supporting meteorological data, they realised they were observing a major weather event, known as a Sudden Stratospheric Warming. On average, these occur every other year and are notoriously unpredictable. This study has shown, for the first time, that cosmic-ray data can be used effectively to identify these events.

Lead scientist for the National Centre for Atmospheric Science, Dr Scott Osprey said: “Up until now we have relied on weather balloons and satellite data to provide information about these major weather events. Now we can potentially use records of cosmic-ray data dating back 50 years to give us a pretty accurate idea of what was happening to the temperature in the stratosphere over this time. Looking forward, data being collected by other large underground detectors around the world, can also be used to study this phenomenon.”
Dr Giles Barr, co-author of the study from the University of Oxford added: “It’s fun sitting half a mile underground doing particle physics. It’s even better to know that from down there, we can also monitor a part of the atmosphere that is otherwise quite tricky to measure”.

Interestingly, the muon cosmic-ray dataset used in this study was collected as a by-product of the MINOS experiment, which is designed to investigate properties of neutrinos, but which also measures muons originating high up in the atmosphere, as background noise in the detector. Having access to these data has led to the production of a valuable dataset of benefit to climate researchers.

Professor Jenny Thomas, deputy spokesperson for MINOS from University College London said “The question we set out to answer at MINOS is to do with the basic properties of fundamental particles called neutrinos which is a crucial ingredient in our current model of the Universe, but as is often the way, by keeping an open mind about the data collected, the science team has been able to find another, unanticipated benefit that aids our understanding of weather and climate phenomena.”

Dr Osprey commented: “This study is a great example of what can be done through international partnerships and cross-disciplinary research. One can only guess what other secrets are waiting to be revealed.”

This offers renewed hope for Svensmark’s theory of cosmic ray modulation of earth’s cloud cover.

Reklamasi Pantura memperparah Banjir di Jakarta ?

Reklamasi Pantura Memperparah Banjir di Jakarta...

Firdaus Cahyadi

Hampir secara berturut-turut harian ini menurunkan berita terkait proyek reklamasi pantai utara Jakarta (Kompas, tanggal 18, 19, dan 21 April 2007). Tentu hal tersebut mengandung maksud agar publik ikut mengkritisi dampak sosial dan lingkungan hidup dari kegiatan proyek yang menelan biaya lebih kurang sebesar Rp 3,499 triliun itu.

Pantai utara (pantura) Jakarta terbentang sepanjang 32 kilometer. Bagian yang akan direklamasi sejauh 1,5 kilometer dari bibir pantai ke arah laut dengan kedalaman maksimal mencapai 8 meter. Reklamasi tersebut dimulai dari sebelah timur perbatasan Cilincing dengan Kabupaten Bekasi hingga sebelah barat perbatasan Penjaringan dengan Kabupaten Tangerang.

Rencananya, di lahan baru tersebut, selain diperuntukkan bagi pembangunan kawasan komersial berupa industri, fasilitas kegiatan pariwisata, perkantoran, dan sarana transportasi, akan dibangun pula kompleks perumahan mewah yang berkapasitas 750.000 orang.

Meskipun ditentang oleh Kementerian Negara Lingkungan Hidup (KLH) melalui Keputusan Menteri (Kepmen) Negara Lingkungan Hidup Nomor 14 Tahun 2003 yang menyatakan ketidaklayakan lingkungan dari proyek reklamasi pantura Jakarta, Pemerintah Provinsi DKI Jakarta dan DPRD tetap bersikeras mengizinkan pengembang untuk mengerjakan proyek itu.

Pemprov dan DPRD DKI menilai proyek ini akan mendatangkan banyak keuntungan ekonomi bagi Jakarta. Logika sederhananya adalah semakin banyak kawasan komersial yang dibangun, dengan sendirinya juga akan menambah pendapatan asli daerah (PAD) kota ini. Di samping itu, reklamasi pantura juga dinilai merupakan wujud dari implementasi Rencana Tata Ruang Wilayah (RTRW) Jakarta yang mengamanatkan kota ini menjadi kota jasa yang berskala nasional dan internasional.

Sayangnya potensi kehancuran ekosistem berupa hilangnya keanekaragaman hayati di Suaka Margasatwa Muara Angke yang merupakan satu-satunya kawasan hutan bakau yang tersisa di Jakarta dan biaya sosial berupa hilangnya akses nelayan terhadap sumber daya alam kelautan tidak pernah masuk dalam perhitungan biaya investasi proyek reklamasi tersebut.

Komentar:
Strategi yang benar untuk menyelamatkan Jakarta :
Pusat pertumbuhan populasi, ekonomi dan industri secara bertahap harus dipindahkan ke luar Jakarta.

(Houw Liong)

22 January 2009

Increasing Atmospheric CO2: Manmade…or Natural?



January 21st, 2009 by Roy W. Spencer, Ph. D.

In fact, it turns out that these large year-to-year fluctuations in the rate of atmospheric accumulation are tied to temperature changes, which are in turn due mostly to El Nino, La Nina, and volcanic eruptions. And as shown in the next figure, the CO2 changes tend to follow the temperature changes, by an average of 9 months. This is opposite to the direction of causation presumed to be occurring with manmade global warming, where increasing CO2 is followed by warming.

From picture (1) This means that most (1.71/1.98 = 86%) of the upward trend in carbon dioxide since CO2 monitoring began at Mauna Loa 50 years ago could indeed be explained as a result of the warming, rather than the other way around.

If natural temperature changes can drive natural CO2 changes (directly or indirectly) on a year-to-year basis, is it possible that some portion of the long term upward trend (that is always attributed to fossil fuel burning) is ALSO due to a natural source?

After all, we already know that the rate of human emissions is very small in magnitude compared to the average rate of CO2 exchange between the atmosphere and the surface (land + ocean): somewhere in the 5% to 10% range. But it has always been assumed that these huge natural yearly exchanges between the surface and atmosphere have been in a long term balance. In that view, the natural balance has only been disrupted in the last 100 years or so as humans started consuming fossil fuel, thus causing the observed long-term increase.

But since the natural fluxes in and out of the atmosphere are so huge, this means that a small natural imbalance between them can rival in magnitude the human CO2 input. And this clearly happens, as is obvious from the second plot shown above!

So, the question is, does long-term warming also cause a CO2 increase, like that we see on in the short term?


http://www.drroyspencer.com/2009/01/increasing-atmospheric-co2-manmade…or-natural/

21 January 2009

Simulation of Convective Cloud Rainfall in Jakarta-Indonesia


Simulation of Convective Cloud Rainfall in Jakarta Using Regional Weather Model





Rahmat Gernowo
Faculty of Mathematics and Natural Sciences, Diponegoro University, Semarang Indonesia#1
Prof. Sudarto Street, Tembalang Semarang.Indonesia
Email: gernowo1@yahoo.com

Bayong Tj. H.K
Atmosphere Science Research Group, Bandung Institute of Technology
Ganesha No. 10 Street, Bandung 40132,Indonesia

The Houw Liong
Physics of Complex System Research Grooup, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology
Ganesha No. 10 Street, Bandung 40132, Indonesia

Tri Wahyu Hadi
Atmosphere Science Research Group, Bandung Institute of Technology
Ganesha No. 10 Street, Bandung 40132,Indonesia


ABSTRACT
The dynamics of rain cloud especially in area of Jakarta represent an important matter in seeking solution, and to prevent floods especially in Jakarta. The research of convection pattern above area of DKI-Jakarta based on meteorological data from high resolution satellite image which is expected to increase the understanding of growth of convection cloud that caused torrential rains and floods in DKI-Jakarta on 2002 and 2007. The special analysis of characteristics of the convective rainfall that initiated the Jakarta Flood event of January/February2002 and 2007 have been done using WRF (weather research and forecasting), a regional numerical weather model developed by Pennsylvania State University/ National Center for Atmospheric Research (PSU/NCAR), with a horizontal grid resolution of 5 km. The global troposphere analysis data from National Center for Environmental Prediction (NCEP) was used as input for the initial and boundary condition. Cloud-top temperature data derived from satellite imageries were utilized to identify the convective clouds. It is found that the temporal and spatial distribution of the simulated convective rainfall during 28 - 31 January 2002 and 31 January - 2 February 2007 are in qualitative agreement with satellite observations of cloud-top temperature variations.
This study contributes to understand extreme rainfalls in DKI-Jakarta. The cloud dynamics in DKI-Jakarta caused by local atmospheric circulation factor and analysis of the model output also revealed that the convective clouds were generated by combined effect of cold-pool advection, mixing layer development, and topography.

19 January 2009

Regional Weather/Climate Models and Weather Modification in Indonesia

Regional Weather/Climate Models and Weather Modification in Indonesia
and Terrestrial Effect of Solar Activity

The Houw Liong2), Plato M.Siregar1), R.Gernowo1), Heru Widodo3)
1) Science Atmosphere Division, Faculty of Earth Science and Technology, ITB
2) Physics of Complex System Division, Faculty of Mathematics and Natural Sciences, ITB
3) UPT Hujan Buatan (Weather Modification Unit), BPPT

Abstract
The current study presents the development of forecasting using numerical weather models ( MM5 and WRF) , and numerical climate models (GCM and DARLAM) and adaptive neuro-fuzzy inference system (ANFIS) for forecasting extreme weather/climate in Indonesia based on sunspot number time series .
Case study will be applied in Jabodetabek region and the impact to severe floods of Ciliwung river . The dynamics of Ciliwung river can be studied by system dynamics.
We study also weather modification to reduce negative impacts of extreme weather. Furthermore we discuss possibility of using ground based generator to fill ground water in transition seasons and to reduce severe floods.
We will discuss also the terrestrial effect of solar activity on climate, power system and communication system when maximum solar activity occurs.
Keywords : numerical weather/climate model, ANFIS, weather modification, ground based generator, terrestrial effect of solar activity.

Floods in Jakarta


West Java is known as the land of two seasons, the rainy season which lasts from November to May and the dry season which lasts from June to October. The administrative area, which is named DKI Jakarta, is now 662 km2 has ten rivers running through it is located on delta comprising three main rivers, the Ciliwung, the Pasanggrahan and the Sunter rivers which annually flood during the monsoon period.
Until the middle of the last century, the Ciliwung river, which is 128 km long, has a 385 km2 basin area and about 75% is outside Jakarta, was the main cause of floods in Jakarta. All flood control works constructed before then were aimed to cope with floods from that river. When a vast tea plantation estate was opened in the upstream part of the Ciliwung river basin, large-scale work was implemented in 1924. A floodway was constructed to divert nearly 300 m3/sec floods westward, out of the city area. Later on the original river channel was reduced in size and connected to some drainage channels which help to carry some of the remaining flood burden into the sea.
The region regularly experiences severe floods such as in 1996,2002 and 2007. The problem of floods in Jakarta came to the forefront after two consecutive severe floods in 1996 , 2002 and 2007. The damage was colossal when floods of severe magnitude. In January 2002 and 2007, the flood inundated 60% of the land area last for about six days and affected thousands of people, and an estimated damage of billions of rupiah. In 1996, the flood was less severe; nevertheless the flood-affected area was about 30% of the land area.
Interception of flood flows from all rivers before entering lowland areas (i.e. the proper city area at the time) was planed by two floodways. The Western Floodway was meant to be an extension of a floodway constructed in 1924 which intercepts the Ciliwung, Cideng and Krukut rivers. The extension was intended to cope with the Grogol, Sekretaris and Angke rivers as well. The Eastern Floodway was aimed to intercept all other remaining rivers (Cipinang, Sunter, Buaran, Jatikramat and Cakung). The floodways were planned to contain 100-year floods, i.e. 290–525 m3/sec for the Western Floodway and 101–340 m3/sec for the Eastern Floodway.

16 January 2009

Natural Forcing vs. Antrophogenic CO2

While all the researchers agreed with the Intergovernmental Panel on Climate Change (IPCC) statement that "warming of the climate system is unequivocal", four of the five disagreed with the claim that the primary cause of the increase was due to anthropogenic emissions of greenhouse gases. The only researcher to agree with the IPCC's assertion was Emori, who is himself a member of the IPCC.

Akasofu countered with the statement, "CO2 emissions have been increasing, but the rise in air temperature stopped around 2001. Climate change is due in large part to naturally occurring oscillations". Akasofu says the earth's warming trend began prior to the industrial age, and believes much of the warming seen may simply be a natural recovery from the so-called Little Ice Age, that ended in the 17th century.

Professor Itoh attacked the temperature record itself, saying "Data taken by the U.S. is inadequate. We only have satellite data of global temperatures from 1979 onwards". Itoh, who has previously called global warming "the worst scientific scandal in history", is also an expert reviewer for the IPCC.

Dr. Kasano believes that cosmic rays, which are modulated by cycles in the strength of the sun's magnetic fields, may potentially have large-scale impacts on the earth's climate.

The report includes the data in which the researchers base their arguments, and can be publicly viewed (in Japanese) on the Internet.

http://www.dailytech.com/Japanese+Report+Disputes+Human+Cause+for+Global+Warming/article13934.htm

Climate Change & Natural Process

Dr. Happer views climate change as a predominately natural process. "The earth's climate is changing now, as it always has. There is no evidence that the changes differ in any qualitative way from those of the past."

In 1991, Happer was appointed director of energy research for the US Department of Energy. In 1993, he testified before Congress that the scientific data didn't support widespread fears about the dangers of the ozone hole and global warming, remarks that caused then-Vice President Al Gore to fire him. "I was told that science was not going to intrude on public policy", he said. "I did not need the job that badly".

Happer's latest remarks were made yesterday, as he asked to be included in a Senate Environment and Public Works report of scientists disputing global warming alarmism. Happer joins 650 other scientists on the list, many of whom have been interviewed previously by DailyTech.

"Computer models used to generate frightening scenarios from increasing levels of carbon dioxide have scant credibility," Happer concluded.

http://www.dailytech.com/Princeton+Physicist+Calls+Global+Warming+Science+Mistaken/article13773.htm

15 January 2009

Bencana tahun 2012 Berdasarkan Prediksi Siklus Matahari ke 24 oleh NASA (Prediction of Solar Cycle 24 by NASA)


Siklus matahari ke 24 yang puncaknya adalah 2012, diperkirakan akan terjadinya CME yang dahsyat sehingga menimbulkan gangguan pada sistem pelistrikan, komunikasi dan navigasi. Walaupun yang mengalami dampak terbesar ialah di sebelah utara dan selatan khatulistiwa karena partikel bermuatan yang disemburkan CME dibelokkan ke arah kutub, namun Indonesia tetap akan terkena dampak sekundernya.

Lebih lagi untuk Jakarta dan berbagai daerah lainnya diperkirakan akan terjadi banjir besar pada saat puncak aktivitas matahari.

The year 2012 will be the peak of solar activity and huge CMEs will cause power systems failure , and disturbances of communication and navigation systems.

Regional Weather/Climate Models in Indonesia

Regional Weather/Climate Models in Indonesia

Plato M. Siregar 1) Deni Septiadi 2) The Houw Liong 3)
1) Science Atmosphere Division, Faculty of Earth Science and Mineral Technology, ITB
2) Climatology Station of Siantan Pontianak, Meteorological and Geophysical Agency, BMG
3) Physics of Complex System Division, Faculty of Mathematics and Natural Sciences, ITB

ABSTRACT
Regional weather/climate in Indonesia is influenced by four main quasi periodic cycles: Solar Activity Cycle (Sunspot Numbers Cycle), Galactic Cosmic Ray Cycle, El Nino Southern Oscillation (ENSO) Cycle, and Indian Ocean Dipole Mode (IOD) Cycle. It can be shown that solar activity cycle can be considered as primary cycle that influence other cycles. In practice eastern Indonesian region is dominantly influenced by ENSO. When the heat pools moves to eastern Indonesian region, then rainfall in this region will be above normal. On the other hand when the heat pool leaves eastern Indonesian region and moves to Pacific Ocean then the rainfall in this region will be below normal.
During a typical Indian Ocean Dipole Mode (IOD) event the weakening and reversal of winds in the central equatorial Indian Ocean lead to the development of unusually warm sea surface temperatures in the western Indian Ocean. IOD negative means wet condition or the rainfall will be above normal along the western Indonesian region.
Precipitation in Pontianak region which represent middle Indonesian region correlated strongly with sunspot numbers cycle (solar activity cycle).
Using ANFIS (Adaptive Neuro Fuzzy Inference System) we are able to predict sunspot numbers cycles so that extreme weather in Indonesian regions can be predicted.
Fuzzy c-means is used to classify regions that are influenced strongly by sunspot numbers (solar activity), IOD, and ENSO cycles. This method is based on fuzzy set as fuzzy c-partition of three cycles above and as cluster center. Fuzzy c-partition matrix for grouping a collection of n data set into c classes.
This study explores the physical of climate predictions and classifications of Indonesian regions and its physical interpretations.

Keywords : ANFIS, fuzzy clustering, climate, solar activity

14 January 2009

The contribution of CO2 to climate change

Renowned climatologist Roger Pielke, Sr. has used IPCC's estimates of climate forcing to calculate the contribution of CO2 to recent climate change. Pielke makes very conservative (worst-case) assumptions in considering the impacts of greenhouse gases, black carbon, tropospheric ozone, and solar radiation. This analysis ignores land use changes, which have been demonstrated to affect climate in a significant way, and cosmic rays, which affect cloud cover and thus can lead to significant climate changes.

Pielke's estimate is that CO2 is responsible for 28% (at most) of the human-caused changes. If natural variations do occur (and it's very hard to argue that they do not) then this value decreases. But even if one assumes that the entire 0.6 deg C increase since 1900 is due to human effects, Pielke's estimate would suggest a CO2 contribution of only 0.17 deg C.
Modern temperatures remain lower than other periods within the Holocene (since the last Ice Age). Geologists and paleoclimatologists believe that the warmest conditions in the Holocene occurred several thousand years before Christ, and that several such episodes occurred. The most recent warm period occurred in medieval times 800-1200 years ago.
Richard A. Muller and Gordon J. MacDonald, “Chapter 1: Brief Introduction to the History of Climate” Ice Ages and Astronomical Causes 2000)

http://icecap.us/docs/faq/

13 January 2009

System Dynamics of Ciliwung River

The Houw Liong






To estimate runoff we have to consider:

a. Debit of ciliwung at Depok can be calculated using rating curve as follows : debit Q = 21.5(TMA+0.22)^1.5 m3/s , TMA = water level when proclaim alert :





Alert I (Siaga I), TMA > 350 cm equivalent to debit 154 m3/s or 555 335 m3/h.






Alert II (Siaga II), TMA > 270 cm equivalent to debit 386 202 m3/h

Alert III ( Siaga III) , TMA > 200 cm is equivalent to debit 71.1 m3/s or 256 017 m3/h.

In an ideal condition (no sedimentation) maximum debit of Ciliwung river at smallest cross section is 600 000 m3 /h. Today debit Ciliwung river at smallest cross section is about 300 000 m3 /h due to sedimentation and houses at the river bank.
b. Floods is cause by high rainfall intensity (> 15 mm/h) that last for hours. Intensity of rainfall 15 mm/h is equivalent to 150 000 m3 / km2.h. Hence if it last for more than 2 hour and extended over more than 50 km2 on Ciliwung basin than the amount of water will be more than 1 500 000 m3, so that Ciliwung will overflow and cause floods. If the intensity is greater than 20 mm/h for more than 3 hours, and extended over more than 100 km2 , the amount of water is more than 6 millions m3.

Runoff could be estimated using system dynamics that based on generalized Lorenz’s differential equations:
dXi/dt = fi ( X1, X2, …) ; i = 1,2,3,4,…
fi can be determined empirically using system dynamics software Vensim , with the rate of rainfall at downstream Ciliwung basin , and river water level / debit at Depok as inputs to the system and the debit at Manggarai as output of the system.
The input debit from the river is given by rating curve:
Q = 21.5(TMA+0.22)^1.5 m3/s
The input from rainfall can be estimated from pentad prediction and spatial and temporal rainfall distribution.
The output debit can be determined empirically i.e. the debit of the river plus a parameter time square root of rainfall intensity.
From the system dynamics we can show precipitation and debit of Ciliwung at Depok as the input the runoff as a function of time and the output debit.
The accuracy of Floods forecasting of Ciliwung can be increased if the space and time distribution of rainfall can be obtained. We also need topography, hydrology data of Ciliwung, and the dynamics of atmosphere.

12 January 2009

Contribution of Cosmic Ray Flux to Warming

On climate response to changes in the cosmic ray flux and radiative budget
Nir J. Shaviv

Racah Institute of Physics, Hebrew University of Jerusalem, Jerusalem, Israel

We examine the results linking cosmic ray flux (CRF) variations to global climate change. We then proceed to study various periods over which there are estimates for the radiative forcing, temperature change and CRF variations relative to today. These include the Phanerozoic as a whole, the Cretaceous, the Eocene, the Last Glacial Maximum, the 20th century, as well as the 11-yr solar cycle. This enables us to place quantitative limits on climate sensitivity to both changes in the CRF, and the radiative budget, F, under equilibrium. Under the assumption that the CRF is indeed a climate driver, the sensitivity to variations in the globally averaged relative change in the tropospheric ionization I script is consistently fitted with μ ≡ − (dT global /d I script) ≈ 7.5 ± 2°K. Additionally, the sensitivity to radiative forcing changes is λ ≡ dT global /dF = 0.35 ± 0.09°KW−1m2, at the current temperature, while its temperature derivative is undetectable with (dλ/dT)0 = −0.01 ± 0.04 m2W−1. If the observed CRF/climate link is ignored, the best sensitivity obtained is λ = 0.54 ± 0.12°KW−1m2 and (dλ/dT)0 = −0.02 ± 0.05 m2W−1. Note that this analysis assumes that different climate conditions can be described with at most a linear function of T; however, the exact sensitivity probably depends on various additional factors. Moreover, λ was mostly obtained through comparison of climate states notably different from each other, and thus only describes an average sensitivity. Subject to the above caveats and those described in the text, the CRF/climate link therefore implies that the increased solar luminosity and reduced CRF over the previous century should have contributed a warming of 0.47 ± 0.19°K, while the rest should be mainly attributed to anthropogenic causes. Without any effect of cosmic rays, the increase in solar luminosity would correspond to an increased temperature of 0.16 ± 0.04°K.

Received 27 October 2004; accepted 1 June 2005; published 23 August 2005.

Citation: Shaviv, N. J. (2005), On climate response to changes in the cosmic ray flux and radiative budget, J. Geophys. Res., 110, A08105, doi:10.1029/2004JA010866.

Konsentrasi CO2 ( CO2 levels)


Konsentrasi CO2 juga menurun, apakah ada mekanisme alam yang menyebabkan penurunan ini ?


Carbon dioxide (CO2) is an important greenhouse gas released through natural processes such as respiration and volcano eruptions and through human activities such as deforestation and burning fossil fuels.. The chart on the left shows the historical levels of CO2 in the Earth's atmosphere. The chart shows CO2 levels in recent years, which have been measured continuously since 1958.

Is the level of CO2 in recent years going down by natural mechanisms ?

http://climate.jpl.nasa.gov/keyIndicators/index.cfm#GlobalTemperature

Anomali Temperatur Global (Global temperature anomaly)



Anomali temperatur global tahun 2007 bukanlah yang tertinggi.
Apakah berarti kecendrungan memanas sudah mulai menurun ?


The time series shows the combined global land and marine surface temperature record from 1850 to 2007. The year 2007 was eighth warmest on record, exceeded by 1998, 2005, 2003, 2002, 2004, 2006 and 2001.

http://climate.jpl.nasa.gov/keyIndicators/index.cfm#GlobalTemperature

Curah hujan puncak di Jakarta pada tahun 2009--Prediksi Banjir Jakarta tahun 2009



Curah hujan puncak untuk Jakarta juga diprediksi dengan ANFIS akan terjadi pada bulan Februari sehingga kemungkinan besar banjir terjadi mulai pertengahan Januari dan Februari dengan genangan berlangsung sekitar 2 hari.

Prediksi Banjir Sungai Ciliwung tahun 2009


Menurut ANFIS dengan memakai deret waktu tinggi muka air rata-rata bulanan berdasarkan data di pos Depok, maka puncak tinggi muka air rata-ratanya ialah bulan Februari, sehingga kemungkinan meluapnya sungai itu dan menimbulkan banjir bagi daerah sekitar sungai itu (kampung Melayu dan Bukit Duri) ialah akhir Januari atau Februari, dengan lama genangan sekitar satu atau dua hari.

11 January 2009

Solar Activity and Climate


Space weather may also in the long term affect the Earth's climate. Solar ultra-violet, visible and heat radiation are the primary factors for the Earth's climate, including global average temperatures, and these energy sources appear to be quite constant. However, many scientists have observed corrrelations between the solar magnetic activity, which is reflected in the sunspot frequency, and climate parameters at the Earth. Sunspots has been recorded through several hundreds of years which makes it possible to compare their variable frequency to climate variations to the extent that reliable climatological records exists. One of the most striking comparisons was published by E. Friis-Christensen og K. Lassen, DMI, in "Science" in 1991. In their work they compared the average temperatureat the northern hemisphere with the average solar activity defined through the interval between successive sunspot maxima. The more active the sun - the shorter the interval: the solar cycle runs more intense. Their results are displayed in the figure above:

The red curve illustrates the solar activity, which is generally
increasing through an interval of 100 years, since the cycle length
has decreased from around 11.5 years to less than 10 years. Within
the same interval the Earth's average temperature as indicated by
the blue curve has increased by approximately 0.7 degree C. Even
the finer structures in the two curves have similar appearances.

(Reference: Friis-Christensen, E., and K. Lassen, Length of the solar
cycle: An indicator of solar activity closely associated with climate,
Science, 254, 698-700, 1991).

Pemeliharaan Lingkungan dan Pengurangan Polusi

Manusia akan menghadapi krisis berat jika tidak mampu memelihara lingkungan hidupnya , menghemat sumber alam dan mengurangi polusi.

Kontribusi setiap orang dapat dilaksanakan dengan melakukan :

1.Reduce and Reuse: Menghemat pemakaian energi dan sumber alam dan materi yang berarti juga mengurangi polusi dan mengurangi kerusakan lingkungan hidup.

2.Recycle: Gunakan kembali benda yang bisa dipakai ulang dan mendaur ulangnya supaya bisa digunakan lagi; tindakan ini akan menghemat sumber alam dan juga mengurangi kerusakan lingkungan.

3.Bantu penghijauan lingkungan kita masing-masing.

10 January 2009

Long Period Climate Change

Milankovitch cycles are the collective effect of changes in the Earth's movements upon its climate, named after Serbian civil engineer and mathematician Milutin Milanković. The eccentricity, axial tilt, and precession of the Earth's orbit vary in several patterns, resulting in 100,000-year ice age cycles of the Quaternary glaciation over the last few million years. The Earth's axis completes one full cycle of precession approximately every 26,000 years. At the same time, the elliptical orbit rotates, more slowly, leading to a 21,000-year cycle between the seasons and the orbit. In addition, the angle between Earth's rotational axis and the normal to the plane of its orbit moves from 22.1 degrees to 24.5 degrees and back again on a 41,000-year cycle. Currently, this angle is 23.44 degrees and is decreasing.
The Milankovitch theory[1] of climate change is not perfectly worked out; in particular, the largest observed response is at the 100,000-year timescale, but the forcing is apparently small at this scale, in regard to the ice ages. Various feedbacks (from carbon dioxide, or from ice sheet dynamics) are invoked to explain this discrepancy.
Milankovitch-like theories were advanced by Joseph Adhemar, James Croll and others, but verification was difficult due to the absence of reliably dated evidence and doubts as to exactly which periods were important. Not until the advent of deep-ocean cores and a seminal paper by Hays, Imbrie and Shackleton, "Variations in the Earth's Orbit: Pacemaker of the Ice Ages", in Science, 1976,[2] did the theory attain its present state.

http://en.wikipedia.org/wiki/Milankovitch_cycles

09 January 2009

Scientists Predict Big Sunspot Cycle Coming

Solar scientists said Monday the next sunspot cycle could be 30 percent to 50 percent stronger than the last one, and it could begin up to a year later than expected – although other scientists dispute the timing prediction.

The peak of the cycle, called the Solar Maximum, generates more frequent magnetic storms and ejections of energetic particles that can slow satellite orbits - thereby interfering with global navigation – as well as disrupt communications and bring down power systems.

During a telephone briefing for reporters Monday, the scientists said they have great confidence in the forecast, because their model has matched the historical data from the past eight solar cycles with more than 98 percent accuracy.

Their Predictive Flux-transport Dynamo Model is enabling NCAR scientists to predict that the next solar cycle, known as Cycle 24, will produce sunspots across an area slightly larger than 2.5 percent of the visible surface of the Sun. They said they expect the cycle to begin in late 2007 or early 2008, which is about six months to 12 months later than a cycle would normally start.Cycle 24 is expected to reach its peak sometime in 2012.

Both groups said the model should help them to forecast sunspot activity for two solar cycles, or 22 years, into the future. The NCAR team is planning in the next year to issue a forecast of Cycle 25, which will peak in the early 2020s.

http://www.spacedaily.com/reports/Scientists_Predict_Big_Sunspot_Cycle_Coming.html

Di Indonesia mungkin akan terkena dampak sekunder, dari kegagalan sistem komunikasi dan navigasi dan juga dampak terhadap cuaca yang akan mengakibatkan banjir besar di Jakarta dan di berbagai tempat.

Chaotic Dynamics

The system sensitivity to initial conditions means that each point in such a system is arbitrarily closely approximated by other points with significantly different future trajectories. Thus, an arbitrarily small perturbation of the current trajectory may lead to significantly different future behaviour.
Sensitivity to initial conditions is popularly known as the "butterfly effect", so called because of the title of a paper given by Edward Lorenz in 1972 to the American Association for the Advancement of Science in Washington, D.C. entitled Predictability: Does the Flap of a Butterfly’s Wings in Brazil set off a Tornado in Texas? The flapping wing represents a small change in the initial condition of the system, which causes a chain of events leading to large-scale phenomena. Had the butterfly not flapped its wings, the trajectory of the system might have been vastly different.

http://en.wikipedia.org/wiki/Chaos_theory

Chaos and Predictability of Weather/Climate Models

The accuracy of predictions of climate/weather models for long range predictions are poor because the atmosphere and ocean system are easily entering chaotic regions.

Chaotic behavior has been observed in the laboratory in a variety of systems including electrical circuits, lasers, oscillating chemical reactions, fluid dynamics, and mechanical and magneto-mechanical devices. Observations of chaotic behavior in nature include the dynamics of satellites in the solar system, the time evolution of the magnetic field of celestial bodies, population growth in ecology, the dynamics of the action potentials in neurons, and molecular vibrations. Everyday examples of chaotic systems include weather and climate. There is some controversy over the existence of chaotic dynamics in the plate tectonics and in economics.
Systems that exhibit mathematical chaos are deterministic and thus orderly in some sense; this technical use of the word chaos is at odds with common parlance, which suggests complete disorder. A related field of physics called quantum chaos theory studies systems that follow the laws of quantum mechanics. Recently, another field, called relativistic chaos, has emerged to describe systems that follow the laws of general relativity.
This article tries to describe limits on the degree of disorder that computers can model with simple rules that have complex results. For example, the Lorenz system pictured is chaotic, but has a clearly defined structure. Bounded chaos is a useful term for describing models of disorder.

http://en.wikipedia.org/wiki/Chaos_theory

08 January 2009

Ice Age vs Cosmic Ray Flux

The Milky Way Galaxy's Spiral Arms and Ice-Age Epochs and the Cosmic Ray Connection

By Nir J. Shaviv
The main result of this research, is that the variations of the flux, as predicted from the galactic model and as observed from the Iron meteorites is in sync with the occurrence of ice-age epochs on Earth. The agreement is both in period and in phase: (1) The observed period of the occurrence of ice-age epochs on Earth is 145 ± 7 Myr (compared with 143 ± 10 Myrs for the Cosmic ray flux variations), (2) The mid point of the ice-age epochs is predicted to lag by 31 ± 8 Myr and observed to lag by 33 ± 20 Myr. This can be seen in the first figure.

A second agreement is in the long term activity: On one hand there were no ice-age epochs observed on Earth between 1 and 2 billion years ago. On the other hand, it appears that the star formation rate in the Milky way was about 1/2 of its average between 1 billion and 2 billion year ago, while it was higher in the past 1 billion years, and between 2 to 3 billion years ago.

Another point worth mentioning is that, unlike some articles which misquote me (or copy from a misquoting article), I don't think we wont have an ice age coming in the coming few tens of millions of years. If this galactic-climate picture is correct (and you should judge yourself from the evidence, in particular by the paper in New Astronomy), it implies that we are at the end of a several 10 million year long "icehouse" epoch during which we have ice-ages come and go, and gradually over the next few millions of years, the severity of ice-ages should diminish, until they will disappear altogether. I wouldn't buy real estate in Northern Canada just yet.

http://www.sciencebits.com/ice-ages

Pulau Jawa akan krisis dalam waktu dekat ?

Gejala yang kita lihat sekarang di pulau Jawa selalu banyak daerah banjir pada musim hujan, tetapi kekurangan air pada musim kering dan intensitasnya bertambah besar.

Jika dianalisis secara teliti dapat ditelusuri dari pertambahan penduduk dan jumlah penduduknya. Pada saat ini jumlah penduduk pulau Jawa sudah mencapai 150 juta penduduk, dan dengan pertambahan sekitar 2% , jumlah penduduk akan menjadi dua kali lipat dalam waktu 70 tahun. Ini berarti dalam 35 tahun pendudk pulau Jawa sudah mencapai 200 juta jiwa. Dalam keadaan sekarang saja pertambahan penduduknya 3 juta jiwa tiap tahun. Dampak jumlah penduduk yang terlalu besar tampak pada kemacetan lalu lintas, pertambahan polusi yang terlalu cepat, rusaknya lingkungan hidup, menipisnya lahan produktif, banjir dan kekeringan.
Jadi menurut model dinamika sistem mungkin dalam 5--10 tahun saja pulau Jawa sudah mengadapi krisis berat.

Slousinya ?

Pindahkan pusat pertumbuhan ekonomi/industri secara bertahap ke luar Jawa, sehingga terjadi transmigrasi spontan dan penduduk pulau Jawa akan berkurang dan pengaturan/pengelolaannya menjadi lebih mudah.

07 January 2009

Penanggulangan Banjir Jakarta

Penanggulangan banjir Jakarta ada yang memakai cara mahal yaitu membangun banjir kanal timur dan membangun terowongan untuk menyimpan air (proyek trilyunan rupiah), atau ada pula cara yang lebih alami yaitu membangun tempat penampungan air dan resapan air dengan melakukan penghijauan pada hulu sungai dan menggalakkan membangun kolam penampungan dan pemanfaatannya supaya dapat dipakai ketika musim kemarau tiba.

Sebetulnya cara kedua lebih wajar untuk dilaksanakan, namun salah satu faktor yang perlu diperhatikan ialah pertambahan jumlah penduduk. Selama pertumbuhan jumlah penduduk Jakarta sangat pesat seperti sekarang pelaksanan penaggulangan banjir sulit dilakukan, tambah lagi dengan masalah polusi dan kemacetan lalu lintas.

Jadi kebijakannya haruslah memindahkan secara bertahap pusat pertumbuhan industri/ekonomi ke luar Jakarta, sehingga dengan sendirinya orang akan pindah ke luar Jakarta dan Jakarta akan lebih mudah diatur dan dikelola.

05 January 2009

Climate Model in Indonesia

CLIMATE MODEL

Climate model can be constructed by using the law of physics for the atmosphere i.e.: The Navier-Stokes equation, the conservation of mass, the conservation of energy, the equations of states and we have to include schemes for cloud formations, carbon and sulfur cycle, interactions between atmosphere and land surface, oceans, cryosphere, and biosphere, furthermore we have to include forcing by volcanic eruptions, the solar activity and galactic cosmic rays.
Researchers from LAPAN using GCM and DARLAM [Ratag, 2002] have reported some results of climate prediction for Indonesian regions. Under a scenario that CO2 concentration doubled in 100 years then the temperature in Indonesian regions will increase on the average about 0.03 degrees Celsius per year. This research showed that the result of prediction of rainfall in these regions is still poor (the correlations on the average are below 0.5) and need some modification on cloud formation scheme.
The second approach can use soft computing methods with the following considerations. The relative positions of the sun in the sky during the seasons, as well as the cycles of solar activity influence the weather and climate throughout the Indonesian archipelago. Solar irradiance and ultraviolet intensity increases with higher solar activity. This in turn will be followed by coronal mass ejection (CME) that increases the charged particles emitted by the sun which could alter the interplanetary magnetic field, and hence the intensity of galactic cosmic rays reaching the earth. The galactic cosmic ray intensity reaching the earth decreases with higher solar activity. Thus the solar activity is often considered as the dominant factor that determines the dynamics of climate [Svensmark, 2007; Landscheidt, 1988]. The dynamics of earth's atmosphere and oceans, evaporation, clouds formation and rainfall, are influenced by the solar energy entering the earth. Several studies indicate that strong correlations exist between the cloud cover and the intensity of galactic cosmic ray reaching the earth [Carlslaw, 2002].
During 1645 – 1715 exceptionally low solar activity (also known as the Maunder minimum) which means high intensity of galactic cosmic ray reached the earth increased cloud cover that led to low temperatures causing what is known as the little ice age.
The present study shows that there is a strong correlation between rainfall in the middle Indonesian region and solar activity and the relation of solar activity and rainfall of other regions. Using this fact we can predict the climate in Indonesian regions by predicting the sunspot numbers (solar activity). It can be shown that to get a good accuracy of predicting a quasi periodic time series as sunspot numbers is possible.
The possibility of reducing the negative effect of climate using weather modification methods is also considered.

Climate Model Based on Sunspot Number


CLIMATE MODEL BASED ON SUNSPOT NUMBER/SOLAR ACTIVITY

The galactic cosmic rays collide with air molecules in the upper atmosphere and produce secondary particles. Generally the charged particles so produced cannot penetrate to lower layers of the atmosphere, except gamma ray, neutrons and the muons. When gamma ray, neutrons and muons interact with the air molecules or water molecules, they become charged and together with aerosols particles act as condensation nuclei for the formation of clouds. The cosmic ray becomes the source of ions in the air besides radiation coming from earth originated by the radio isotope radon.
During the sunspot minimum, the intensity of the galactic cosmic ray that penetrates earth atmosphere becomes maximum which in turn increases the coverage of clouds. This implies that solar irradiation reaching the earth will be minimized. Conversely, during solar activity maximum or sunspot maximum, the intensity of galactic cosmic ray reaching lower levels of the atmosphere decreases, less cloud condensation nuclei are produced, hence the cloud cover decreases, furthermore extra energy received from flares during prominent eruptions, maximizes the amount of solar energy reaches the earth.
Although global cloud cover produces a warming effect or the greenhouse effect, but a cooling effect due to reflections against direct solar irradiation is more dominant factor [Svensmark,2007].
Furthermore during solar activity maximum, the intensity of ultraviolet that penetrates the earth increases. Solar activity maximum usually is followed by increasing coronal mass ejection. Both effects caused greater amount of energy penetrates the earth and this will influence the climate through the dynamics of the atmosphere and oceans.
Using rainfall data in Indonesia from NCEP Reanalysis at
http://www.cdc.noaa.gov/cdc/data.ncep.reanalysis
and sunspot numbers time series, we can get relations between sunspot numbers and rainfall in various Indonesian regions. The determination of sunspot numbers on yearly basis against the yearly rainfall for various regions in Indonesia based on time series data are shown in Figures 3, 4 and 5 .
From Figure 3 we can conclude that eastern Indonesia (Jayapura region) which represented Eastern Indonesian Maritime Continent is strongly influenced by ENSO.
After 1976 sunspot numbers maximum SMax and sunspot numbers minimum SMin correspond to precipitations above normal also to La Nina and maximum eruptions CME corresponding to precipitations below normal and also to El Nino.[4, 5] In Pontianak region which represent middle Indonesian Maritime Continent, the yearly precipitation is mainly determined by sunspot cycles (Figure 4). Precipitations above normal occur at sunspot maximum SMax, and precipitations below normal at sunspot minimum SMin. Precipitations in east Indonesia which represent North Australia Indonesian Monsoon are influenced by ENSO similar to those observed in Jayapura region. (Figure 3) Precipitations in Jakarta region or Jabodetabek are weakly influenced by ENSO. The peaks of yearly precipitations correspond to the peaks of sunspot numbers, but at the sunspot numbers minimum which correspond to galactic comic ray maximum, the yearly precipitations also maximum.
The west Indonesian region is mainly influenced by IOD that also correlated to solar cycle.
The fuzzy c-means clustering shows that the western Indonesian region is influenced mainly by IOD, the eastern Indonesian region is influenced mainly by ENSO and the middle region is mainly influenced by solar activity.
So, by knowing sunspot number time series as predicted by ANFIS and fuzzy clustering of climate regions we can predict the coming extreme weather for each regions in Indonesia

Prediksi Jangka Panjang Banjir Besar Jakarta


Berdasarkan analisis deret waktu sunspot, curah hujan Jabodetabek, tinggi muka air sungai Ciliwung dan Pasanggrahan dengan ANFIS, dapat disimpulkan bahwa Jakarta akan banjir setiap tahun pada bulan Desember,Januari atau Februari, dan setiap kali banjir akan tergenang selama sekitar 2 hari terutama untuk daerah yang dekat dengan sungai Ciliwung dan Pasanggrahan.
Banjir besar seperti tahun 2002 terjadi ketika sunspot maksimum, dan diprakirakan akan terjadi lagi sekitar tahun 2012, sedangkan banjir seperti tahun 1996 dan 2007 terjadi ketika sunspot minimum/sinar kosmik maksimum, dan diperkirakan akan terjadi lagi sekitar tahun 2018. Ketika banjir besar genangan air akan berlangsung sekitar 6 hari, dan bisa meluas sehingga 70% daerah Jakarta kena genangan air.

04 January 2009

Analisis Prediksi Banjir Sungai Ciliwung

Sains, Filsafat Sains dan Teknologi


Analisis Prediksi Banjir DAS Ciliwung

Model prediksi banjir dengan metode Jaringan Saraf Tiruan dan logika samar belum peka terhadap fluktuasi curah hujan yang sangat besar, karena bencana banjir tidak semata–mata ditentukan oleh curah hujan tetapi faktor lain juga sangat penting. Pendangkalan dan penyempitan WAS menyebabkan kapasitas sungai terhadap curah hujan berkurang. Pendangkalan Sungai Ciliwung yang mencapai 1,5 m dan penyempitannya dari 60 m menjadi sekitar 20 m, akan menyebabkan peluapan air sungai untuk jumlah curah hujan sama.
Prediksi jangka pendek dengan akurarsi lebih baik beberapa hari sebelum kejadian dapat dilakukan dengan tepat melalui model atmosfer dan model curah hujan – limpasan. Namun model ini memerlukan masukan dari radar cuaca atau satelit cuaca serta data topografi, tutupan lahan , koefisien kekasaran dan saluran air.
Prediksi 6—10 jam sebelum banjir bandang ( banjir kiriman dari hulu Ciliwung) sudah dilakukan oleh PWSCC dengan sistem telemetri yang memantau ketinggian muka air. Tinggi muka air lebih besar dari 2m di Depok yang berlangsung beberapa jam akan menyebabkan luapan air di bantaran sungai sekitar Bukit Duri dan Kampung Melayu.

Sistem Peringatan Dini sungai Ciliwung




Sistem Peringatan Dini Banjir Sungai Ciliwung
The Houw Liong1)
R.Gernowo2)
P.M Siregar2)
Heru Widodo3)

1) FMIPA, ITB
2) FITB, ITB
3) TMC, BPPT.

Abstrak
Dengan memakai adaptive neuro-fuzzy inference system (Anfis) prakiraan banjir dapat dilakukan setahun sebelumnya dengan melihat ekstrapolasi deret waktu dari bilangan sunspot, enam bulan sebelumnya dengan menganalisa deret waktu hujan bulanan dan tinggi muka air bulanan, dan dua minggu sebelumnya dengan menganalisa deret waktu pentad hujan dan tinggi muka air.
Untuk prediksi banjir kiriman yang berjangka pendek 8 jam sebelumnya dapat dilakukan dengan sistem telemetri tinggi muka air.
Sistem peringatan dini beberapa hari sebelumnya memerlukan model matematik curah hujan-limpasan dari aliran daerah aliran sungai dan prediksi curah hujan dengan memakai model atmosfer disertai dengan data satelit mengenai topografi , parameter meteorologi dan data radar cuaca.

Banjir Das Ciliwung
Dari inventarisasi bencana alam banjir DAS Ciliwung yang melintasi wilayah Jakarta dan sekitarnya, maka banjir berskala besar terjadi jika hujan lebat turun terus menerus selama 2 jam atau lebih. Bencana alam banjir yang melanda daerah Jakarta dan sekitarnya disebabkan oleh hujan torensial.
Hipotesis prediksi banjir DAS Ciliwung hilir (di daerah Jakarta) ialah sbb. : Prediksi banjir DAS Ciliwung hilir dapat dilakukan dengan memakai data deret waktu tinggi muka air (TMA) sungai Ciliwung di Depok dan data deret waktu curah hujan (CH) rata-rata DKI dibantu dengan probabilitas intensitas curah hujan DKI.
Data TMA dan CH diurut menjadi deret waktu yang kemudian dibuat moving average untuk menghilangkan noisenya. Prediksinya dipakai metode ANFIS(Adaptive Neuro Fuzzy Inference System). Model prediksi banjir yang dibangun kemudian diuji dengan data lapangan, sehingga diperoleh validasi model yang dapat diterapkan untuk DAS Ciliwung hilir.
Untuk membangun model banjir yang baik diperlukan data penyerapan dan penguapan air , khususnya untuk DAS Ciliwung hilir faktor hanya berpengaruh sekitar 5% saja, karena kejadian banjir berlangsung relatif cepat.
Untuk prediksi jangka panjang dipakai anomali bilangan sunspot. Dalam selang waktu 9 bulan sampai 12 bulan sebelum kejadian dapat diperkirakan apakah DAS ciliwung akan dipengaruhi oleh La Nina atau El Nino. Prediksi jangka menengah dilakukan berdasarkan deret waktu data tinggi muka air sungai (TMA) bulanan dan curah hujan bulanan sekitar daerah aliran sungai (DAS) Ciliwung. Metoda ini diharapkan dapat memprediksi jangka menengah (3—6 bulan ) , lalu berdasarkan pentad akan diprediksi jangka pendek (1—3 minggu) sebelum kejadian, sehingga dapat disiapkan secara tepat keperluan untuk mengatasi banjir yang akan melanda DAS Ciliwung yang melintasi Jakarta.

03 January 2009

Climate Model Based on Solar Activity

Sains, Filsafat Sains dan Teknologi

CLIMATE MODEL BASED ON SOLAR ACTIVITY

The galactic cosmic rays collide with air molecules in the upper atmosphere and produce secondary particles. Generally the charged particles so produced cannot penetrate to lower layers of the atmosphere, except gamma ray, neutrons and the muons. When gamma ray, neutrons and muons interact with the air molecules or water molecules, they become charged and together with aerosols particles act as condensation nuclei for the formation of clouds. The cosmic ray becomes the source of ions in the air besides radiation coming from earth originated by the radio isotope radon.
During the sunspot minimum, the intensity of the galactic cosmic ray that penetrates earth atmosphere becomes maximum which in turn increases the coverage of clouds. This implies that solar irradiation reaching the earth will be minimized. Conversely, during solar activity maximum or sunspot maximum, the intensity of galactic cosmic ray reaching lower levels of the atmosphere decreases, less cloud condensation nuclei are produced, hence the cloud cover decreases, furthermore extra energy received from flares during prominent eruptions, maximizes the amount of solar energy reaches the earth.
Although global cloud cover produces a warming effect or the greenhouse effect, but a cooling effect due to reflections against direct solar irradiation is more dominant factor [Svensmark,2007].
Furthermore during solar activity maximum, the intensity of ultraviolet that penetrates the earth increases. Solar activity maximum usually is followed by increasing coronal mass ejection. Both effects caused greater amount of energy penetrates the earth and this will influence the climate through the dynamics of the atmosphere and oceans.
Using rainfall data in Indonesia from NCEP Reanalysis at
http://www.cdc.noaa.gov/cdc/data.ncep.reanalysis
and sunspot numbers time series, we can get relations between sunspot numbers and rainfall in various Indonesian regions. The determination of sunspot numbers on yearly basis against the yearly rainfall for various regions in Indonesia based on time series data are shown in Figures 3, 4 and 5 .
From Figure 3 we can conclude that eastern Indonesia (Jayapura region) which represented Eastern Indonesian Maritime Continent is strongly influenced by ENSO.
After 1976 sunspot numbers maximum SMax and sunspot numbers minimum SMin correspond to precipitations above normal also to La Nina and maximum eruptions CME corresponding to precipitations below normal and also to El Nino.[4, 5] In Pontianak region which represent middle Indonesian Maritime Continent, the yearly precipitation is mainly determined by sunspot cycles (Figure 4). Precipitations above normal occur at sunspot maximum SMax, and precipitations below normal at sunspot minimum SMin. Precipitations in east Indonesia which represent North Australia Indonesian Monsoon are influenced by ENSO similar to those observed in Jayapura region. (Figure 3) Precipitations in Jakarta region or Jabodetabek are weakly influenced by ENSO. The peaks of yearly precipitations correspond to the peaks of sunspot numbers, but at the sunspot numbers minimum which correspond to galactic comic ray maximum, the yearly precipitations also maximum.
The west Indonesian region is mainly influenced by IOD that also correlated to solar cycle.
The fuzzy c-means clustering shows that the western Indonesian region is influenced mainly by IOD, the eastern Indonesian region is influenced mainly by ENSO and the middle region is mainly influenced by solar activity.
So, by knowing sunspot number time series as predicted by ANFIS and fuzzy clustering of climate regions we can predict the coming extreme weather for each regions in Indonesia

Numerical Climate Model

Sains, Filsafat Sains dan Teknologi


NUMERICAL CLIMATE MODEL

Climate model can be constructed by using the law of physics for the atmosphere i.e.: The Navier-Stokes equation, the conservation of mass, the conservation of energy, the equations of states, including schemes for cloud formations, carbon and sulfur cycle, interactions between atmosphere and land surface, oceans, cryosphere, and biosphere, furthermore we have to include forcing by volcanic eruptions, the solar activity and galactic cosmic rays.
In Indonesia scientists from LAPAN [Ratag, 2002] use Global circulation Model (GCM) with 9 levels vertical resolution and 3.20x5.60 horizontal resolution based on sigma coordinates. Limited local area model (DARLAM) is used to obtain higher resolution 25 km x 25 km. The model also has to include schemes for cloud formations, carbon and sulfur cycle, and interactions between atmosphere and land surface, oceans, cryosphere, and biosphere.
With a scenario that the concentration of CO2 will be doubled in 100 years, the temperature anomaly can be calculated. In 50 years from 1990 most Indonesian regions the temperature very likely will raise by 0.5 to 1.0 degrees Celsius. Some regions the temperature very likely will raise by 1.0 to 1.5 degrees Celsius. This climate model cannot predict the precipitations in Indonesian regions well. However the verification of this forecasting is not convincing because the in Indonesia many good weather stations are located in cities. From these stations the average increase of temperature from 1860 to 2000 is about 10 , but actually the increase is due to urban warming, not global warming.
The forecasting of rainfall is still poor, for instant using rainfall data from Bandung the best forecasting is obtained using cloud formation according to Arakawa scheme the correlation with the data is 0.46.
It needs some modification before it can be use to predict rainfall well.
Furthermore a realistic model has to include forcing by volcanic eruptions, the solar activity and galactic cosmic rays which is poorly represented in climate model.
Due to the nature of chaotic system [Lorenz, 1960] and the difficulties to formulate complete model, and to get accurate data of boundary conditions and initial conditions are difficult, the accuracy of long term prediction is poor.

Climate Model in Indonesian Region


Sains, Filsafat Sains dan Teknologi


Climate in Indonesian Regions and Weather Modification
The Houw Liong2), Plato M.Siregar1), Heru Widodo3)

1) Science Atmosphere Division, Faculty of Earth Science and Mineral Technology, ITB
2) Physics of Complex System Division, Faculty of Mathematics and Natural Sciences, ITB
3) UPT Hujan Buatan (Weather Modification Unit), BPPT

Abstract
According to researchers in LAPAN (Indonesian Space and Aeronautics Institute) Global Circulation Model (GCM) and Limited Area Climate Model (DARLAM) can be used to predict climate in Indonesian regions. With a scenario that the concentration of CO2 will be doubled in 100 years, the temperature anomaly can be calculated. In 50 years from 1990 most Indonesian regions the temperature very likely will raise by 0.5 to 1.0 degrees Celsius. Some regions the temperature very likely will raise by 1.0 to 1.5 degrees Celsius. This climate model cannot predict the precipitations in Indonesian regions well. It needs some modification before it can be use to predict the precipitations well.
We know that climate model can be classified as weak causality therefore the accuracy of prediction is good only for short time prediction.
The second approach to predict climate in Indonesia is based on soft computing by knowing that the climate in Indonesian regions is influenced by four main quasi periodic cycles: Solar Activity Cycle (Sunspot Numbers Cycle), Galactic Cosmic Ray Cycle, El Nino Southern Oscillation (ENSO) Cycle, and Indian Ocean Dipole Mode (IOD) Cycle. It can be shown that solar activity cycle can be considered as primary cycle that influence other cycles.
In practice eastern Indonesian region is dominantly influenced by ENSO. When the heat pools moves to eastern Indonesian region, then rainfall in this region will be above normal. On the other hand when the heat pool leaves eastern Indonesian region and moves to Pacific Ocean then the rainfall in this region will be below normal.
During a typical Indian Ocean Dipole Mode (IOD) event the weakening and reversal of winds in the central equatorial Indian Ocean lead to the development of unusually warm sea surface temperatures in the western Indian Ocean. IOD negative means wet condition or the rainfall will be above normal along the western Indonesian region.
Precipitation in Pontianak region which represent middle Indonesian region correlated strongly with sunspot numbers cycle (solar activity cycle).
Using ANFIS (Adaptive Neuro Fuzzy Inference System) we are able to predict sunspot numbers cycles about 10 years from now so that climate and extreme weather in Indonesian regions can be predicted.
Fuzzy c-means is used to classify regions that are influenced strongly by sunspot numbers (solar activity), IOD, and ENSO cycles. This method is based on fuzzy set as fuzzy c-partition of three cycles above and as cluster center. Fuzzy c-partition matrix for grouping a collection of n data set into c classes.
This study explores the physics of climate predictions, its physical interpretations and weather modification to reduce the negative effect of climate change.

Keywords: climate model, ANFIS, fuzzy clustering, climate, solar activity, weather modification.

Global Cooling ?

Sains, Filsafat Sains dan Teknologi

Increase twice the size of Germany: "colder weather" to blame. 

Data from the National Snow and Ice Data Center (NSIDC) has indicated a dramatic increase in sea ice extent in the Arctic regions. The growth over the past year covers an area of 700,000 square kilometers: an amount twice the size the nation of Germany.

With the Arctic melting season over for 2008, ice cover will continue to increase until melting begins anew next spring.

The data is for August 2008 and indicates a total sea ice area of six million square kilometers. Ice extent for the same month in 2007 covered 5.3 million square kilometers, a historic low. Earlier this year, media accounts were rife with predictions that this year would again see a new record. Instead, the Arctic has seen a gain of about thirteen percent.

William Chapman, a researcher with the Arctic Climate Research Center at the University of Illinois, tells DailyTech that this year the Arctic was "definitely colder" than 2007. Chapman also says part of the reason for the large ice loss in 2007 was strong winds from Siberia, which affect both ice formation and drift, forcing ice into warmer waters where it melts.


http://www.dailytech.com/Arctic%20Sees%20Massive%20Gain%20in%20Ice%20Coverage/article12851.htm

Model Dinamika Sistem dan Krisis Dunia

Sains, Filsafat Sains dan Teknologi

Model Dinamika Sistem dan Krisis Dunia

Sekitar tahun 1960 -- 1970 Jay Forrester seorang guru besar di MIT mencetuskan model dinamika system yang dapat dipakai untuk menganalisis aliran barang pada industri dan kelompoknya berhasil memperluasnya untuk menganalisis dinamika system dunia. Kelompok yang dipimpinya  mengambil populasi/jumlah penduduk , hasil industri dan pertanian, sumber alam dan lahan subur, polusi dan sampah sebagai kuantitas penting yang perlu diperhatikan.

Jumlah penduduk dunia naik sekitar 2% per tahun, meningkatnya jumlah penduduk secara eksponensial ini yang berarti populasi akan menjadi dua kali lipat setiap 70 tahun. Jumlah penduduk dunia ada sekitar 6 milyar dalam tahun 2000 dan kalau pertumbuhanya tetap 2%, maka dalam tahun 2070 akan menjadi 12 milyar manusia.

Populasi meningkat secara eksponensial berarti kebutuhan pangan, pakaian dan papan/perumahan akan meningkat, ini berarti hasil pertanian dan industri perlu ditingkatkan secara eksponensial pula, akibatnya sumber alam dan luas tanah subur akan menipis, polusi dan sampah akan bertambah, kalau ini berlangsung terus maka dunia akan menuju krisis.

Model ini menunjukkan bahwa tanpa strategi dan tindakan yang tepat sekitar tahun 2050 saja mungkin menusia sudah krisis air bersih, menipisnya sumber alam dan kerusakan lingkungan yang parah.

Memang model ini banyak dikritik dan ditunjukkan terdapat kelemahan, namun kecenderungan umum bahwa tanpa strategi dan tindakan yang tepat untuk menaganinya maka dunia akan menuju krisis.

Pemanasan Global dan Krisis Dunia

Sains, Filsafat Sains dan Teknologi

Pemanasan Global dan Krisis Dunia

The Houw Liong, Kelompok Keahlian Fisika Sistem Kompleks, FMIPA, ITB

Jurnal Transformasi, Vol 4,No.2, Agustus 2008

Dalam ayat 1:27 – 30  Kitab Kejadian manusia diciptakan secara khusus dan manusia diberi tanggung jawab untuk berkembang biak dan memelihara lingkunganya dalam proses pengembangan yang berkelanjutan.

Namun apakah yang terjadi dalam zaman sekarang ? Pertambahan penduduk yang terlalu cepat disertai dengan menipisnya sumber alam dan polusi yang berlebihan menyebabkan perkembangan dunia yang menuju krisis.

Suhu rata-rata global pada permukaan Bumi telah meningkat 0,74 ± 0,18 °C selama seratus tahun terakhir (1900 – 2000). Intergovernmental Panel on Climate Change (IPCC) menyimpulkan bahwa, "sebagian besar peningkatan temperatur rata-rata global sejak pertengahan abad ke-20 kemungkinan besar disebabkan oleh meningkatnya konsentrasi gas-gas rumah kaca  terutama akibat aktivitas manusia yaitu pemancaran gas karbondioksida (CO2)  dan metana (CH4) oleh kendaraan bermotor dan pabrik/industri" melalui efek rumah kaca. Kesimpulan dasar ini telah dikemukakan oleh setidaknya 30 badan ilmiah dan akademik, termasuk semua akademi sains nasional dari negara-negara yang industrinya maju. 

Namun, masih terdapat banyak ilmuwan yang tidak setuju dengan beberapa kesimpulan yang dikemukakan IPCC tersebut. Ilmuwan ini berpendapat bahwa siklus alam seperti siklus aktivitas matahari, siklus letusan gunung api yang dahsyat dan siklus sinar kosmik galaktik dan respons biosfer lebih berpengaruh daripada akibat aktivitas manusia.