Global Satellite Mapping Of Precipitation Data Research Articles

Evaluation of GSMaP Data for Extreme Rain Events and Causing Floods in East Kotawaringin

On 12 May 2021, 12 August 2021, 6 September 2021 and 27 June 2022, extreme rain occured with an intensity of 58.85 mm/day, 101.3 mm/day, 124.4 mm/day and 176.8 mm/day respectively in East Kotawaringin. These phenomena occurred during the dry season and caused flooding, which is a rare condition during the dry season in East Kotawaringin. This study aims to evaluate extreme rainfall using GSMaP (Global Satellite Mapping of Precipitation) data, where analysis using GSMaP has never been done before in East Kotawaringin. These GSMaP data were processed and compared with the observation data from the Meteorological Station of H. Asan, East Kotawaringin. After that, the GSMaP rainfall results are verified using statistical methods, namely RMSE, correlation coefficient and bias. The verification results show that the bias gives underestimate results for all dates. In addition, the RMSE values on 12 May 2021, 12 August 2021, 6 September 2021 and 27 June 2022 are 10.83, 17.32, 12.41 and 34.03, respectively. These high RMSE values indicate that the GSMaP rainfall value is quite far from the observed rainfall value. The correlation value between GSMaP rainfall and observations has a high correlation with values of 0.84, 0.90, 0.96 and 0.98 for each date. These results show that the GSMaP data has a good correlation value and can be used for extreme rainfall analysis at the Meteorological Station of H. Asan, East Kotawaringin.

A Two-Step Approach to Blending GSMaP Satellite Rainfall Estimates with Gauge Observations over Australia

An approach to developing a blended satellite-rainfall dataset over Australia that could be suitable for operational use is presented. In this study, Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates were blended with station-based rain gauge data over Australia, using operational station data that has not been harnessed by other blended products. A two-step method was utilized. First, GSMaP satellite precipitation estimates were adjusted using rain gauge data through multiplicative ratios that were gridded using ordinary kriging. This step resulted in reducing dry biases, especially over topography. The adjusted GSMaP data was then blended with the Australian Gridded Climate Dataset (AGCD) rainfall analysis, an operational station-based gridded rain gauge dataset, using an inverse error variance weighting method to further remove biases. A validation that was performed using a 20-year range (2001 to 2020) showed the proposed approach was successful; the resulting blended dataset displayed superior performance compared to other non-gauge-based datasets with respect to stations as well as displaying more realistic patterns of rainfall than the AGCD in areas with no rain gauges. The average mean absolute error (MAE) against station data was reduced from 0.89 to 0.31. The greatest bias reductions were obtained for extreme precipitation totals and over mountainous regions, provided sufficient rain gauge availability. The newly produced dataset supported the identification of a general positive bias in the AGCD over the north-west interior of Australia.

Improving GSMaP V06 precipitation products over the Upper Tocantins River basin in the Brazilian Cerrado, based on local rain-gauge network

Gridded precipitation products from remote sensing are currently available and could potentially enhance the use of precipitation data in regions with sparse network of ground rain gauges. Thus, this study aimed to evaluate the performance and propose correction models for Global Satellite Mapping of Precipitation (GSMaP) use in the Upper Tocantins River basin, a key area for food and electricity production in central Brazil, but with sparse network of ground rain gauges, challenging water resources and agriculture decision makers. GSMaP data were compared with data from a rain gauge network between 2000 and 2019. Evaluations were made at daily and monthly temporal scales. In general, GSMaP products show an overestimate bias for drizzle (0.1 ~ 1 mm day−1) and underestimate for rainfalls above 10 mm day−1. The use of monthly scale data significantly reduces the bias observed in the daily scale, but with an underestimation trend of -28.3% and -39.7% for the dry and rainy periods, respectively. Categorical indices showed that the GSMaP system had better hit rates for rain detection in the rainy season (October–April) than in the dry season (May–September). For the studied region, the use of GSMaP data on daily and monthly scales should be preceded by a bias analysis as a function of rain gauge network data. The use of bias coefficient corrected observed rainfall data underestimation on daily and monthly scales, improved correlation between GSMaP and observed rainfall data and reduced errors associated with rainfall network data within the basin influence area. The findings of this study indicate how decision makers could adjust and apply GSMaP products to estimate rainfall for water resources, agriculture and drought management challenges in the Upper Tocantins River basin.

A Comparison Between Global Satellite Mapping of Precipitation Data and High-Resolution Radar Data – A Case Study of Localized Torrential Rainfall over Japan

This paper presents a case study comparing the latest algorithm version of Global Satellite Mapping of Precipitation (GSMaP) data with C-band and X-band Multi-Parameter (MP) radar as high-resolution rainfall data in terms of localized heavy rainfall events. The study also obliged us to clarify the spatial and temporal resolution of GSMaP data using high-accuracy ground-based radar, and evaluate the performance and reporting frequency of GSMaP satellites. The GSMaP_Gauge_RNL data with less than 70 mm/day of daily rainfall was similar to the data of both radars, but the GSMaP_Gauge_RNL data with over 70 mm/day of daily rainfall was not, and the calibration by rain-gauge data was poor. Furthermore, both direct/indirect observations by the Global Precipitation Measurement/Microwave Imager (GPM/GMI) and the frequency thereof (once or twice) significantly affected the difference between GPM/GMI data and C-band radar data when the daily rainfall was less than 70 mm/day and the hourly rainfall was less than 20 mm/h. Therefore, it is difficult for GSMaP_Gauge to accurately estimate localized heavy rainfall with high-density particle precipitation.

Relationship between extreme rainfall based on GSMaP data with Madden Julian Oscillation (MJO) in Bangka Island

The effect of MJO events on extreme rainfall events on Bangka Island needs to be spatially studied in more detail. The low quality of rain station data encourages the use of alternative data such as Global Satellite Mapping of Precipitation (GSMaP). This study aimed to analyze the influence of active MJO on extreme rainfall events using GSMaP data on Bangka Island. The methods used are extreme threshold of 95th, 98th and 99th percentile, the Spearman Rank correlation between extreme rainfall events and active MJO and its correlation significance test. The results showed that northern coast has the highest range of extreme threshold. The correlation significance test showed GSMaP grids that have a significant correlation with the MJO are in the north of the Island, especially coast area. MJO phases 3 and 5 have an impact on decreasing of extreme rainfall events which are known through the negative correlation values. From all the three extreme thresholds, it was only MJO phase 4 that increasing extreme rainfall events. Active MJO phase 4 causes Bangka Island as a convective area, while MJO phases 3 and 5 affect the formation of suppressed areas on Bangka Island.

Estimation of Rainfall Rate Cumulative Distribution in Indonesia Using Global Satellite Mapping of Precipitation Data

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Evaluation and inter-comparison of high-resolution multi-satellite rainfall products over India for the southwest monsoon period

ABSTRACTRainfall is one of the key drivers of the global hydrological cycle and has large socio-economic impacts. Tropical rainfall accounts for two-thirds of the global rainfall and is primarily associated with the monsoon. Multi-satellite rainfall products provide rainfall with high temporal and spatial resolutions; however, they exhibit regional and seasonal biases. Evaluation of these products against ground-based observations can improve the accuracy of the estimated rainfall. With the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high-resolution multi-satellite precipitation products namely; Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) are released. In the present study the spatial and temporal structures of rainfall in near real time and research versions of IMERG-V4 (near real-time (NRT) & Final (FNL)), GSMaP-V6 (NRT & moving vector with Kalman filter (MVK)), INSAT3D (Indian National Satellite System (INSAT) Multispectral Rainfall Algorithm (IMR) & Hydro-Estimator method (HEM)) and Indian Meteorological Department (IMD) – National Centre for Medium Range Weather Forecasting (NCMRWF) Merged product have been evaluated against gridded gauge-based IMD rainfall data on daily, monthly and seasonal scales. All the datasets show noticeable bias in producing rainfall over orographic regions (i.e. Western Ghats and foothills of Himalayas) and North-East India, though there exists significant difference among the satellite measurements. Different skill scores are computed for GSMaP, IMERG and INSAT3D data products to evaluate the performance of these satellite estimates. However in terms of biases IMD-NCMRWF Merged, GSMaP (NRT & MVK) and IMERG (NRT & FNL) underestimates rainfall (about 11%, 17%, 23%, 18% and 3%, respectively) and INSAT3D (IMR & HEM) overestimates (about 49% and 33%, respectively), for the India region as a whole. In a similar way, HEM product shows 15% better performance than IMR product in INSAT3D category. However, both NRT and MVK products of GSMaP show similar variations compared to observe rainfall. Overall IMD–NCMRWF merged and IMERG-FNL data products show better agreement with the gauge-based IMD data compared to GSMaP. The GPM-based products (IMERG and GSMaP) estimate rainfall much better than INSAT3D estimation.

Online Model Parameter Estimation With Ensemble Data Assimilation in the Real Global Atmosphere: A Case With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Global Satellite Mapping of Precipitation Data

AbstractThis study aims to improve precipitation forecasts by estimating model parameters of a numerical weather prediction model with an ensemble‐based data assimilation method. We implemented the parameter estimation algorithm into a global atmospheric data assimilation system NICAM‐LETKF, which incorporates Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF). This study estimated a globally uniform model parameter of a large‐scale condensation scheme known as the B1 parameter of Berry's parameterization. We conducted an online estimation of the B1 parameter using the Global Satellite Mapping of Precipitation (GSMaP) data and successfully reduced NICAM's precipitation forecast bias relative to the GSMaP data, especially for weak rains. The estimated B1 parameter evolved toward the optimal value obtained by manual tuning. The parameter estimation also mitigated a dry bias for the lower troposphere in the Tropics. However, the estimated B1 intensified biases for cloud water mixing ratio and outgoing long‐wave radiation in the regions where shallow clouds are dominant. This is because only precipitation data were used to estimate the optimal value of B1, and more constraints will be required to obtain a suitable value for climatological simulations.

Analysis of Rainfall Data based on GSMaP and TRMM towards Observations Data in Yogyakarta

In reality, to obtain a complete and representative rainfall observations data in a particular area is quite difficult. Therefore, to complement the existence of rainfall data, satellite data is used, so the rainfall data can got by us for a region that has limited equipment, then the characteristics of rainfall can knew by us in that region. The purpose of this study is to analyzed the validity of GSMaP (Global Satellite Mapping of Precipitation) and TRMM (Tropical Rainfall Measuring Mission) data with in-situ rainfall data in Yogyakarta during the period (2012-2016). The method used is to process GSMaP and TRMM data into daily, decade, monthly, and seasonal data to compare with in situ data and perform statistical tests. Based on the results it can be seen that during the observation period for DJF (December-January-February), the highest GSMaP and TRMM rainfall correlations occur in decade-1 and decade-3 respectively. In MAM (March-April-May), GSMaP (decade-2) and TRMM (decade-1), while in JJA (June-July-August) for GSMaP (decade-2) and TRMM (decade-3), and in SON (September-October-November) for GSMaP (decade-3) and TRMM (decade-2). Therefore it can be concluded that the correlation between GSMaP and TRMM with in situ data, the best occurs on average in decade-2 and decade-3.

Evaluation and Bias Correction of Satellite-Based Rainfall Estimates for Modelling Flash Floods over the Mediterranean region: Application to Karpuz River Basin, Turkey

This study investigates the utility of satellite-based rainfall estimates in simulating flash floods in Karpuz River Basin, Turkey, characterized by limited rain gauge network. Global Satellite Mapping of Precipitation (GSMaP) product was evaluated with the rain gauge network at daily and monthly time-scales considering seasonality, elevation zones, extreme events and rainfall intensity thresholds. Statistical analysis indicated that GSMaP shows acceptable linear correlation coefficient with rain gauges, however, suffers from significant underestimation bias. Statistical measures exhibited a remarkable deterioration with increasing elevation-following a linear relationship; for example, percent bias was found to increase by a rate of 11.7% with every 400 m interval. A multiplicative bias correction scheme was devised, and Hydrological River Basin Environmental Assessment Model (Hydro-BEAM) was implemented to simulate flash floods driven by the uncorrected/corrected GSMaP data. Analysis of intensity thresholds revealed that appropriate threshold selection is critically important for the bias correction procedure. The hydrological model was calibrated for flash flood events during October–December 2007 and 2012 and validated during October–December, 2009 and 2010. Flash floods simulations were improved by the local bias correction procedure applied to the GSMaP data, but the degree of improvement varied from one period to another. The results of the study indicate that bias factors incorporating multiple variables such as extreme events and elevation variability have the potential to further improve flood simulations.

Investigating the impacts of typhoon-induced floods on the agriculture in the central region of Vietnam by using hydrological models and satellite data

Flooding associated with landing tropical cyclones (TCs) is one of the major natural hazards in the coastal region of Vietnam. Annually, approximately 5 or 6 TCs make landfall in Vietnam, bringing heavy rains and inducing flooding, particularly to the central coastal region because of its topography and geographic configuration. This study focuses on the modelling of typhoon-induced floods that have resulted in widespread damage to agriculture over the central Thua Thien Hue Province of Vietnam by coupling two well-known hydrological models, KINEROS2 and HEC-RAS (Daniel et al. in Open Hydrol J 5(1), 2011), and using GSMaP (Global Satellite Mapping of Precipitation) data as the satellite rainfall input. Landsat imagery and GIS are also used for mapping and analysing the inundated areas. The discharge and water level from the KINEROS2 and HEC-RAS models displayed acceptable results for the floods modelled from three selected typhoons; both the Nash–Sutcliffe simulation efficiency coefficient (NSE) and the coefficient of determination (R2) were greater than 0.6. The simulated inundation maps of these typhoon-induced floods were compared with those extracted from the Landsat imagery to assess consistency. The result revealed a similar spatial extension of the inundated agricultural areas. This information, together with the forecasted TC movements and associated rainfalls, will be helpful to plan methods for mitigating potential typhoon-induced flooding and damage, particularly damage to agricultural regions.

Error features of the hourly GSMaP multi-satellite precipitation estimates over nine major basins of China

AbstractAs one of the current mainstream satellite precipitation estimates, the Global Satellite Mapping of Precipitation (GSMaP) system of Japan has been developed to produce high-precision and high-resolution global rainfall products by integrating almost all of the available precipitation-related satellite sensors. To quantify the error features of GSMaP estimates and understand their hydrological potentials at short temporal scale, three widely used GSMaP products (GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge) were comprehensively investigated at 1 hourly and 0.1° × 0.1° resolutions over nine major basins of China. Assessment results show that GSMaP_NRT apparently underestimates the rainfall amounts, while GSMaP_MVK with both forward and backward propagation processes algorithm can effectively capture the most rainfall events and has the lower error and bias. GSMaP_Gauge displays the best error stability and error structure over most basins of China and also exhibits stronger rain-rate dependencies. However, its unexpected positive biases in southeastern basins, which mainly come from the overestimation at lower rain rates, still need to improve further in future developments. We expected the results documented here can both provide the retrieval developers with some valuable references and offer hydrologic users of GSMaP data a better understanding of their error features and potential utilizations for various hydrological applications.

Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)

AbstractThis study aims to propose two new approaches to improve precipitation forecasts from numerical weather prediction (NWP) models through effective data assimilation of satellite‐derived precipitation. The assimilation of precipitation data is known to be very difficult mainly because of highly non‐Gaussian statistics of precipitation variables. Following Lien et al., this study addresses the non‐Gaussianity issue by applying the Gaussian transformation (GT) based on the empirical cumulative distribution function (CDF) of precipitation. We propose a method that constructs the CDF with only recent 1 month samples, without using a long period of samples needed previously. We also propose a method to use the inverse GT, with which we can obtain realistic precipitation fields from biased NWP model outputs. We assimilate the Japan Aerospace eXploration Agency's Global Satellite Mapping of Precipitation (GSMaP) data into the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at 112 km horizontal resolution. Assimilating the GSMaP data results in improved weather forecasts compared to the control experiment assimilating only rawinsonde data. We find that horizontal observation thinning is necessary, probably due to the horizontal observation‐error correlations in the GSMaP data. We also obtained precipitation fields similar to GSMaP from the NICAM precipitation forecasts by using the inverse GT, leading to an improved precipitation forecast.

Nowcasting with Data Assimilation: A Case of Global Satellite Mapping of Precipitation

Abstract Space–time extrapolation is a key technique in precipitation nowcasting. Motions of patterns are estimated using two or more consecutive images, and the patterns are extrapolated in space and time to obtain their future patterns. Applying space–time extrapolation to satellite-based global precipitation data will provide valuable information for regions where ground-based precipitation nowcasts are not available. However, this technique is sensitive to the accuracy of the motion vectors, and over the past few decades, previous studies have investigated methods for obtaining reliable motion vectors such as variational techniques. In this paper, an alternative approach applying data assimilation to precipitation nowcasting is proposed. A prototype extrapolation system is implemented with the local ensemble transform Kalman filter and is tested with the Japan Aerospace Exploration Agency’s Global Satellite Mapping of Precipitation (GSMaP) product. Data assimilation successfully improved the global precipitation nowcasting with the real-case GSMaP data.

A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region

Summary Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high resolution multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) version 6 are released. A critical evaluation of these newly released precipitation data sets is very important for both the end users and data developers. This study provides a comprehensive assessment of IMERG research product and GSMaP estimates over India at a daily scale for the southwest monsoon season (June to September 2014). The GPM-based precipitation products are inter-compared with widely used TRMM Multi-satellite Precipitation Analysis (TMPA), and gauge-based observations over India. Results show that the IMERG estimates represent the mean monsoon rainfall and its variability more realistically than the gauge-adjusted TMPA and GSMaP data. However, GSMaP has relatively smaller root-mean-square error than IMERG and TMPA, especially over the low mean rainfall regimes and along the west coast of India. An entropy-based approach is employed to evaluate the distributions of the selected precipitation products. The results indicate that the distribution of precipitation in IMERG and GSMaP has been improved markedly, especially for low precipitation rates. IMERG shows a clear improvement in missed and false precipitation bias over India. However, all the three satellite-based rainfall estimates show exceptionally smaller correlation coefficient, larger RMSE, larger negative total bias and hit bias over the northeast India where precipitation is dominated by orographic effects. Similarly, the three satellite-based estimates show larger false precipitation over the southeast peninsular India which is a rain-shadow region. The categorical verification confirms that these satellite-based rainfall estimates have difficulties in detection of rain over the southeast peninsula and northeast India. These preliminary results need to be confirmed in other monsoon seasons in future studies when the fully GPM-based IMERG retrospectively processed data prior to 2014 are available.