scholarly journals STUDY OF GPM-IMERG RAINFALL DATA PRODUCT FOR GANGOTRI GLACIER

2018 ◽  
Vol XLII-5 ◽  
pp. 383-388
Author(s):  
P. Verma ◽  
S. K. Ghosh
Keyword(s):  
Field Data ◽  
Monsoon Season ◽  
Meteorological Data ◽  
Data Product ◽  
Precipitation Measurement ◽  
Post Monsoon Season ◽  
Gangotri Glacier ◽  
New Generation ◽  
Global Precipitation ◽  
Rainy Days

<p><strong>Abstract.</strong> This study presents a comparison of new generation weather observatory satellites Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) rainfall products with field data collected for Gangotri glacier in India. The meteorological analysis of rainfall estimates has been performed on GPM IMERG Final, Late and Early precipitation products available at daily scale with a spatial resolution of 0.1&amp;deg;<span class="thinspace"></span>&amp;times;<span class="thinspace"></span>0.1&amp;deg; for melting season from May to September for the year 2014 and 2015 respectively. The comparison of satellite products with field data was done using correlation coefficient and standard anomaly. The Late run curve showed a high degree of similarity with final run curve while early run showed variation from them. The satellite meteorological data correctly identified non-rainy days with an average of &amp;sim;86.7%, &amp;sim;67.5% and &amp;sim;95% for pre-monsoon, monsoon and post-monsoon season respectively. The rmse for final run data product for 2014 and 2015 are 4.5, 1.23, 1.55, 1.24, 0.8 and 1.14, 7.1, 1.82, 1.15, 1.52 from May to September respectively. Overall, it has been observed that for medium to heavy rainfall final run estimates are close to field data and for light to medium rainfall late run estimates are close. Similar results have been obtained from both datasets for non-rainy days in the study area.</p>

An Analysis and Example of Use of the GPM Gridded Text Products

2020 ◽  
Author(s):  
Erich Franz Stocker ◽  
Owen Kelley ◽  
Jason West
Keyword(s):  
Indian Monsoon ◽  
Monsoon Season ◽  
Space Resolution ◽  
Data Product ◽  
Precipitation Measurement ◽  
Scientific Analysis ◽  
Ascii Format ◽  
Level 3 ◽  
Global Precipitation Measurement ◽  
Global Precipitation

&lt;p&gt;This poster provides the design, content and purpose of the&lt;br&gt;Global Precipitation Measurement (GPM) gridded text&lt;br&gt;products. Gridded text products at the same time and space resolution are&lt;br&gt;available from the start of the TRMM period in January 1998 through the&lt;br&gt;current GPM data collection period. The poster provides an example of the&lt;br&gt;use of this data product by examining the structure of the Indian monsoon as&lt;br&gt;well as examining the monsoon during El Nino and La Nina periods. It will&lt;br&gt;also look at diurnal precipitation during the Indian monsoon season. As&lt;br&gt;part of the examination of the Indian monsoon using the gridded text&lt;br&gt;product, the poster demonstrates the ease of integration with other data.&lt;br&gt;In this case, Sea Surface Temperature (SST) data that is relevant to the Indian monsoon is examined&lt;br&gt;side-by-side with the precipitation data. It further demonstrates the ease&lt;br&gt;of aggregating the daily gridded data across many years while still&lt;br&gt;retaining the hourly structure that enables diurnal studies. The GPM&lt;br&gt;gridded text product is currently the only level 3 GPM product which can&lt;br&gt;be aggregated in this way. The representation of data in ASCII format&lt;br&gt;allows potential users to concentrate on the scientific analysis rather&lt;br&gt;than the physical format of the data. In summary, the poster provides an&lt;br&gt;overview that uses examples to demonstrate the efficacy of this unique GPM&lt;br&gt;data product.&lt;/p&gt;

scholarly journals Evaluation of GPM-Era Satellite Precipitation Products on the Southern Slopes of the Central Himalayas Against Rain Gauge Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1836 ◽  
Author(s):  
Shankar Sharma ◽  
Yingying Chen ◽  
Xu Zhou ◽  
Kun Yang ◽  
Xin Li ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Dynamic Range ◽  
Precipitation Amount ◽  
Rain Gauge ◽  
Central Himalayas ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
New Generation ◽  
Global Precipitation Measurement ◽  
Global Precipitation

The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monsoonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (<30%) and mostly underestimated (by >40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal.

scholarly journals Spatial variability of annual and monthly Potential Evapotranspiration (PET) over India

MAUSAM ◽  
2021 ◽  
Vol 63 (4) ◽  
pp. 581-586
Author(s):  
I. J. VERMA ◽  
V.K. SONI ◽  
N.D. SABALE ◽  
A.L. KOPPAR
Keyword(s):  
Potential Evapotranspiration ◽  
Monsoon Season ◽  
Meteorological Data ◽  
Central India ◽  
Western India ◽  
Desert Area ◽  
Post Monsoon ◽  
Post Monsoon Season ◽  
The Mean ◽  
Monteith Equation

In this study, meteorological data for well distributed 140 locations in India for the period (1971-2005) have been utilized for estimation of potential evapotranspiration (PET) by Penman-Monteith equation. The highest average annual PET of 2342 mm was at Jalgaon and lowest of 921 mm at Ging. Range of average annual PET is 1421 mm. The mean annual PET averaged for all stations over India is 1547 mm with 12% contribution in winter, 34% in pre-monsoon, 35% in monsoon and 19% in post-monsoon seasons. The lowest centers with annual PET less than 1400 mm are mainly located above 30 degree N latitude. The high centers with annual PET more than 1800 mm are located in desert area and central India, with lowest values at hill stations during most of the months. The higher monthly PET values in excess of 200 mm are normally observed during pre-monsoon and monsoon over western and Central India. As the monsoon advances, the PET values over western India decrease gradually. The lower PET values are observed during winter and post-monsoon season. The lowest mean monthly PET of 82.1 mm is in December and highest mean monthly PET of 199.6 mm is in May. Mean annual and monthly PET over (2° × 2°) latitude/longitude grids have been developed and presented.

scholarly journals TRMM Microwave Imager (TMI) Alignment and Along-Scan Bias Corrections

2018 ◽  
Vol 35 (7) ◽  
pp. 1457-1470 ◽  
Author(s):  
Rachael Kroodsma ◽  
Stephen Bilanow ◽  
Darren McKague
Keyword(s):  
Processing System ◽  
Tropical Rainfall Measuring Mission ◽  
Temperature Bias ◽  
Data Product ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Bias Corrections

AbstractThe Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) dataset released by the Precipitation Processing System (PPS) has been updated to a final version following the decommissioning of the TRMM satellite in April 2015. The updates are based on increased knowledge of radiometer calibration and sensor performance issues. In particular, the Global Precipitation Measurement (GPM) Microwave Imager (GMI) is used as a model for many of the TMI updates. This paper discusses two aspects of the TMI data product that have been reanalyzed and updated: alignment and along-scan bias corrections. The TMI’s pointing accuracy is significantly improved over prior PPS versions, which used at-launch alignment values. A TMI instrument mounting offset is discovered as well as new alignment offsets for the two TMI feedhorns. The original TMI along-scan antenna temperature bias correction is found to be generally accurate over ocean, but a scene temperature-dependent correction is needed to account for edge-of-scan obstruction. These updates are incorporated into the final TMI data version, improving the quality of the data product and ensuring accurate geophysical parameters can be derived from TMI.

scholarly journals Evaluation of GPM Dual-Frequency Precipitation Radar (DPR) Rainfall Products Using the Rain Gauge Network over China

2020 ◽  
Author(s):  
Yang Gao ◽  
Tongwen Wu ◽  
Jun Wang ◽  
Shihao Tang
Keyword(s):  
Pearson Correlation ◽  
Rain Gauge ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Precipitation Measurement ◽  
Observation Network ◽  
New Generation ◽  
Global Precipitation Measurement ◽  
Rain Rates ◽  
Global Precipitation

AbstractThe Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite provides the new-generation global observation of rain since 2014. The main objective of this paper is to evaluate the suitability and limitation of GPM-DPR level-2 products over China. The DPR rain rate products are compared with rain gauge data during the summers of five years (2014-2018). The ground observation network is composed of more than 50000 rain gauges. The DPR precipitation products for all scans (DPR_NS, DPR_MS and DPR_HS) generally underestimate rain rates. However, DPR_MS agrees better with gauge estimates than DPR_NS and DPR_HS, yielding the lowest mean error, systematic deviation, and the highest Pearson correlation coefficient. In addition, all three swath types show obvious overestimation over gauge estimates between 0.5 to 1 mm/h and underestimation when gauge estimates are larger than 1 mm/h. The DPR_HS and DPR_MS agree better with gauge estimates below and above 2.5 mm/h, respectively. A deeper investigation was carried out to analyze the variation of DPR_MS’s performance with respect to terrains over China. An obvious underestimation, relative to gauge estimates, occurs in Tibetan Plateau while a slight overestimation occurs in North China Plain. Furthermore, our comprehensive analysis suggests that in Sichuan Basin, the DPR_MS exhibit the best agreement with gauge estimates.

scholarly journals Trend assessment in climate variable by Mann Kendall test of Bastar district of Chhattisgarh

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 79-82
Author(s):  
RAJESH KHAVSE ◽  
J.L. CHAUDHARY
Keyword(s):  
Linear Trend ◽  
Anthropogenic Activities ◽  
Monsoon Season ◽  
Meteorological Data ◽  
Kendall Test ◽  
Atmospheric Temperature ◽  
Natural Phenomenon ◽  
Trend Test ◽  
Agricultural Planning ◽  
Rainy Days

Climate change is a natural phenomenon but in present decades its variability of change mainly due to anthropogenic activities is alarming. Agriculture of Chhattisgarh state is mainly dependant on monsoon rain and its distribution. Considering this fact, the present study  has been tried to analyze the most important climatic variables,              viz., precipitation and temeperature for analyzing their trend in the area. The trends of maximum atmospheric temperature, rainfall and rainy days are analysed statistically for meteorological data of Jagdalpur station of Bastar district, over last three decades stretching between years 1980 to 2014. The long term change in temperature, rainfall and rainy days has been analysed by correlation and linear trend analysis. The annual MMAX temperature has decreased at a rate of -0.465 °C per year during this period at Jagdalpur station and decreasing trend for rainy days during monsoonal season (June to September) is also found and is confirmed by Mann-Kendall trend test. Very weak increasing trend is observed in total month rainfall (TMRF) during season June to September. There are decreasing trends of mean monthly rainfall and south west (June - September) rainfall observed in Bastar district of Chhattisgarh. The agricultural planning and utilization of water is dependent on monsoon rainfall and more than 75% of rainfall occurring during the monsoon season is uneven both in time and space. Therefore its analysis is important for crop planning.  

scholarly journals Monsoon, post monsoon rainy days analysis by using normal, binomial distribution and discrete probability for south Gujarat

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 37-58
Author(s):  
NEERAJ KUMAR ◽  
S.K. CHANDRAWANSHI
Keyword(s):  
Binomial Distribution ◽  
Monsoon Season ◽  
Chi Square ◽  
Discrete Probability ◽  
Post Monsoon ◽  
Post Monsoon Season ◽  
Chi Square Test ◽  
June July ◽  
Result Analysis ◽  
Rainy Days

The analysis will be conducted for standard weekly (SW) 22 to 47 of monsoon and post monsoon season at south Gujarat. The standard weekly rainy days analysis of binomial distribution for monsoon season of Navsari on chi-square test on binomial distribution was found in standard week (SW) 22 to 31, 33 and standard week (SW) 35 to 39 and post monsoon in standard week (SW) 41 to 44 shows significant. The result also reveals that the monsoon season SW 32 and 34 and post monsoon season SW 40, 45, 46 and 47 revealed non-significant result. Analysis reveals the rainfall is not equally distributed during SW 32, 34, 40, 45, 16 and 47, so that the test of binomial distribution is a good fit. Monsoon season rainfall data of Navsari, Bharuch and Valsad reveals that the normal distribution at 10, 20 and 30% probability levels for the month of June, July, August and September shows the possibility of increasing rainy days occurrence. The Navsari and Bharuch districts during post monsoon season rainfall of months of October and November reveals decreasing tendency except Valsad district. The binomial distribution fit only those standard weeks in which rainfall is not equally distributed. The standard weekly rainy days analysis of binomial distribution on chi-square test in Bharuch was found that standard week (SW) 25 only 10% of monsoon season and in post monsoon standard week (SW) 42 and 47 shows non significant (5 and 10% level of significant) result, but SW 25 found significant at 5% level. In case of Valsad district, standard week 22 to 39 of monsoon season and in post monsoon season 41, 42, 43 and 46 standard weeks shows significant result. The result reveals that the monsoon season of Bharuch standard weeks 22 to 39 except from 25 and post monsoon 40, 41, 43, 44, 45 and 46 shows significant result. Further, in Valsad district standard weeks 40, 44, 45 and 47 shows significant result. The trend analysis of rainy days shows that increasing trend in monsoon season and decreasing trend in post monsoon season of Navsari, Bharuch and Valsad districts. From above results observed that the rainfall distribution is not equally distributed so test of binomial distribution at above given standard week is a good fit. The data also shows that, decreasing tendency in rainfall was observed except Valsad district. 

scholarly journals Trend Detection in Rainfall and Temperature Data [1960-2014] of Anand, Gujrat, India

2021 ◽  
pp. 125-133
Author(s):  
S. S. Chinchorkar ◽  
G. J. Kamani
Keyword(s):  
Linear Trend ◽  
Monsoon Season ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Kendall Test ◽  
Trend Detection ◽  
Growth Stages ◽  
Climate Risks ◽  
Mann Kendall Test ◽  
Rainy Days

The temperature and rainfall trends are analyzed for meteorological data of Anand in Gujarat, India over approximately last three decades stretching between years 1960 to 2014. The long–term change in temperature and rainfall has been assessed by linear trend analysis. Due to their biophysical characteristics, dry lands ecosystems are most Vulnerable the Climate risks. Climate variability has serious implications on major livelihoods of the region i.e. Agriculture and livestock. In this paper, attempts have been made to study variations in temperature and rainfall in Anand of Gujarat, India. Data at annual, seasonal and monthly time scales for the period of 1960-2014 (Temperature) and 1960-2014 (Rainfall) were examined. Study of monthly variations revealed rise in the temperatures in the month of September. Rainfall and Rainy days have also increased in past 4 decades. Annual and Monsoon rainfall have been observed to increase, where the month of August shows a statistically significant increasing trend. Any variability in monsoon season will have implications on agricultural activities as the season overlaps with Kharif, a major cropping season for the country. The variations of temperature and rainfall during monsoons may have impacts on the various growth stages of the crops. Changing weather conditions may lead to increase in pest infestations. Macro level studies may or may not be relevant at village level and therefore the advisories generated may not benefit the locals. Trends in temperature, rainfall and rainy days have been assessed by Non-parametric tests (Mann-Kendall or Pre Whitened Mann-Kendall test for trend detection and Theil and Sen's Slope for magnitude of trend). Temperature and Rainfall variations, Climate Change, Mann-Kendall Test.

PERBANDINGAN SEBARAN CURAH HUJAN HASIL OBSERVASI SATELIT GPM DENGAN MODEL CUACA WRF (Studi Kasus: Siklon Tropis Cempaka Dan Dahlia, 27 Nov – 3 Des 2017)

2019 ◽  
Vol 3 ◽  
pp. 1063
Author(s):  
Fatkhuroyan Fatkhuroyan
Keyword(s):  
Weather Research And Forecasting ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Japan Aerospace Exploration Agency ◽  
Weather Research

Satelit GPM (Global Precipitation Measurement) merupakan proyek kerjasama antara NASA (National Aeronautics and Space Administration) dan JAXA (Japan Aerospace Exploration Agency) serta lembaga internasional lainnya untuk membuat satelit generasi terbaru dalam rangka pengamatan curah hujan di bumi sejak 2014. Model Cuaca WRF (Weather Research and Forecasting) merupakan model cuaca numerik yang telah dipakai oleh BMKG (Badan Meteorologi Klimatologi dan Geofisika) untuk pelayan prediksi cuaca harian kepada masyarakat. Pada tanggal 27 November – 3 Desember 2017 telah terjadi bencana alam siklon tropis Cempaka dan Dahlia di samudra Hindia sebelah selatan pulau Jawa. Tujuan Penelitian ialah untuk mengetahui sebaran akumulasi curah hujan antara observasi satelit GPM dan model cuaca WRF, serta keakuratan model WRF terhadap observasi satelit GPM saat terjadinya bencana alam tersebut. Metode yang dipakai ialah dengan melakukan analisa meteorologi pertumbuhan terjadinya siklon tropis tersebut hingga terjadinya hujan sangat lebat secara temporal maupun spasial. Dari hasil analisa disimpulkan bahwa satelit GPM memiliki luasan sebaran curah hujan yang lebih kecil daripada sebaran hujan model cuaca WRF pada saat siklon tropis Cempaka dan Dahlia. Bias akumulasi sebaran hujan model cuaca WRF juga cukup bagus terhadap satelit GPM sehingga dapat dilakukan antisipasi dampak hujan lebat yang terjadi.

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