Estimating rainfall variations in space and time is a key aspect of drought early warning and environmental monitoring. An evolving drier-than-normal season must be placed in a historical context so that the severity of rainfall deficits can be quickly evaluated. However, estimates derived from satellite data provide areal averages that suffer from biases due to complex terrain, which often underestimate the intensity of extreme precipitation events. Conversely, precipitation grids produced from station data suffer in more rural regions where there are less rain-gauge stations. CHIRPS was created in collaboration with scientists at the USGS Earth Resources Observation and Science (EROS) Center in order to deliver complete, reliable, up-to-date data sets for a number of early warning objectives, like trend analysis and seasonal drought monitoring.
Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. Spanning 50S-50N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05 resolution satellite imagery, and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.
Download Data Curah Hujan Shp
DOWNLOAD 🔥 https://urllie.com/2y5z2A 🔥
Aplikasi DATA ONLINE - PUSAT DATABASE - BMKG adalah aplikasi layanan data untuk pengguna,baik untuk kalangan internal BMKG maupun eksternal yang terdiri dari kalanganPerguruan Tinggi, Institusi Kementrian/Lembaga, Swasta, dan Masyarakat pengguna dataMKKuG
Analisis hujan yang disajikan pada halaman 2 s/d 5 menunjukkan kondisi faktual curah hujan yang terjadi selama bulan Oktober 2023. Analisis ini dilakukan berdasarkan data observasi dari stasiun BMKG, pos hujan kerja sama yang tersebar di seluruh wilayah Indonesia dan data satelit Global Satellite Mapping of Precipitation (GSMaP).
Rainfall data analysis is the first stage of a water resource planning. One of rainfall data analysis method is using rain frequency analysis. In this research, rainfall frequency analysis is used to prediction the probability of occurrence from hydrological event. The maximum monthly rainfall frequency distribution is affects to rainfall during high repeat periods. Rainfall is the amount of water that falls on a flat surface during certain repetitive periods. Secondary data is got from Temindung Station of Samarinda City on 2007 to 2016. The type of distribution are used Normal, Gumbel, Log Pearson Type III, and Log Normal. Compatibility test of Non Parametric Statistics using Chi Square method. The results showed if the estimated rainfall at the highest repeating period of 2, 5, 10, 25, 50, and 100 years is Log Normal distribution. The distribution that requirement of qualify criteria is Log Normal and Gumbel distribution. The distribution that fit from Chi Square test is Gumbel distribution is 3,5177 and Log Normal distribution is 6,8945. From Kolmogorov Smirnov test the value of Gumbel distribution is 0, and Log Normal distribution is 0,0805. Rainfall patterns for Normal distribution, Gumbel distribution, Pearson Log distribution Type III and Log Normal distribution are horizontal patterns.
Indikasi fenomena perubahan iklim di Indonesia dapat diamati dari perubahan pola curah hujan rata-rata di beberapa wilayah di Indonesia. Guna mengidentifikasi wilayah-wilayah yang mengalami perubahan pola curah hujan jangka panjang di Indonesia, maka BMKG mengeluarkan produk informasi Perubahan Normal Curah Hujan dalam bentuk atlas.
Perubahan normal curah hujan memuat informasi perubahan/ deviasi terhadap normal curah hujan 30 tahun di Indonesia. Data yang digunakan adalah data curah hujan rata-rata bulanan dari periode tahun 1980-2010. di Indonesia. Dalam gafik diperlihatkan perubahan/penyimpangan pola curah hujan dari normalnya pada 10 tahun terakhir di Indonesia.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms and any other similar reproductions, as well as translations, the reproduction of any part of this journal, its storeage in databases and its transmission by any form or media. such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc.
ABSTRAK: Ketersediaan pos stasiun hujan yang kurang memadai untuk pencatatan data curah hujan sebagai data hidrologi menjadi salah satu permasalahan dalam perencanaan bangunan air. Permasalahan ini dapat diselesaikan dengan bantuan data curah hujan TRMM. TRMM (Tropical Rainfall Measurement Mission) merupakan misi NASA untuk melakukan pemantauan curah hujan tropis dengan menggunakan teknologi satelit pemantau. Namun, data TRMM harus divalidasi terlebih dahulu. Analisis validasi dilakukan untuk mengetahui kesesuaian data curah hujan TRMM dengan curah hujan wilayah. Sub DAS Sumber Brantas digunakan sebagai studi kasus dengan pertimbangan ketersedian data yang dianggap memadai. Metode validasi yang digunakan yaitu Root Mean Squared Error (RMSE), NashSutcliffe Efficiency (NSE), Koefisien Korelasi (R), dan Uji Kesalahan Relatif (KR). Analisis validasi dilakukan dengan dua perhitungan, yaitu validasi data tidak terkoreksi dan validasi data terkoreksi yang melalui tahap kalibrasi, verifikasi, dan validasi. Hasil analisis menunjukan validasi data terkoreksi memiliki nilai yang lebih baik dibandingkan dengan data tidak terkoreksi. Analisis keseluruhan menunjukan data TRMM dapat digunakan sebagai alternatif data hidrologi.
ABSTRACT: The availability of ground stations that are inadequate for recording rainfall data as hydrological data is one of the problems in water building planning. This problem can be solved with the help of TRMM rainfall data. TRMM (Tropical Rainfall Measurement Mission) is NASA's missions to monitor tropical rainfall by using weather monitoring satellite technology. However, TRMM data must be validated first. Validation analysis was conducted to determine the suitability of TRMM rainfall data with regional rainfall. The Sumber Brantas Sub-Watershed was used as a case study considering the availability of data that was considered adequate. The validation method used Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (R), and Relative Error Test (KR). In the validation analysis carried out with two calculations, uncorrected data and corrected data validation through the stages of calibration, verification, and validation. The results of the analysis showed that corrected data validation has a better value than the uncorrected data. The overall analysis showed that TRMM data can be used as an alternative to hydrological data.
The main difference between the IMERG Early and Late Run is that Early only has forward propagation (which basically amounts to extrapolation), while the Late has both forward and backward propagation (allowing interpolation). The additional 10 hours of latency allows lagging data transmissions to make it into the Late run, even if they were not available for the Early.
We always advise people to use the Final Run for research unless their application will require the use of Early or Late data due to latency. In such a case, the application should be developed using the long-record of the Early or Late, as appropriate. The vast majority of grid boxes have fairly similar Late and Final values over ocean, and to a lesser extent over land. Extreme value statistics are more sensitive to these details; medians, means, and root-mean square difference are less sensitive.
3GPROF, 'GPROF Profiling', produces global 0.25 degree x 0.25 degree gridded means using Level 2 Gprof data. Vertical hydrometeor profiles and surface rainfall means are computed. Various pixel counts are also reported. The PI is Joyce Chou. The product can be monthly or daily.
As of the GPM Version 6 reprocessing cycle, the radars on both the TRMM and GPM satellites have their data products written in the HDF5 file format. Also as of Version 6 the research products are stored in the same FTP archive for both satellites, The FTP archive is organized into directories whose names are "yyyy/mm/dd/radar/" where yyyy, mm, and dd are the four-digit year and the two-digit month and day of month, respectively. In prior reprocessing cycles, TRMM and GPM data products were stored in different FTP archives. As of May 2020, PPS distributes near-realtime GPM data via FTPS and HTTPS rather than FTP. A similar switch is expected to occur with research data products later in 2020.
GPM Dual-frequency Precipitation Radar (DPR) and TRMM Precipitation Radar (PR) single-orbit rainfall estimates. The objective of these radar algorithms is to generate f radar-only derived meteorological quantities on an instantaneous FOV (field of view) basis working from the level 1 radar products. A subset of these estimates serves as input data to the level 2 combined radar-radiometer algorithm and the level 3 combined and radar-only products. The general idea behind these level 2 algorithms is to determine general characteristics of the precipitation, correct for attenuation, and estimate profiles of the precipitation water content, rainfall rate. When dual-wavelength data are available, the algorithm also estimates the particle size distributions in the phase of the precipitation (i.e., liquid or frozen). GPM's dual-wavelength data will provide better estimates of rainfall and snowfall rates than those of the TRMM PR data.
For instruments currently in orbit, there are near-realtime (NRT) products and standard-research products. All instruments have climate products that are designated 2A-CLIM GPROF or 2A-CLIM PRPS. The difference between climate products and standard-research products is that climate products use ancillary data that are not produced until approximately 3 months after the satellite observations are made.
Level 1A: Reconstructed, unprocessed instrument data at full resolution, time referenced, and annotated with ancillary information, including radiometric and geometric calibration coefficients and georeferencing parameters (i.e., platform ephemeris), computed and appended, but not applied, to Level 0 data. 17dc91bb1f
family and friends 4 class book pdf