scholarly journals Evaluation of MERRA-2 Precipitation Products Using Gauge Observation in Nepal

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 40 ◽  
Author(s):  
Kalpana Hamal ◽  
Shankar Sharma ◽  
Nitesh Khadka ◽  
Binod Baniya ◽  
Munawar Ali ◽  
...  
Keyword(s):  
Land Surface ◽  
Precipitation Amount ◽  
Ground Surface ◽  
High Elevation ◽  
Rain Gauge ◽  
Important Variable ◽  
Statistical Measures ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Potential Alternative

Precipitation is the most important variable in the climate system and the dominant driver of land surface hydrologic conditions. Rain gauge measurement provides precipitation estimates on the ground surface; however, these measurements are sparse, especially in the high-elevation areas of Nepal. Reanalysis datasets are the potential alternative for precipitation measurement, although it must be evaluated and validated before use. This study evaluates the performance of second-generation Modern-ERA Retrospective analysis for Research and Applications (MERRA-2) datasets with the 141-gauge observations from Nepal between 2000 and 2018 on monthly, seasonal, and annual timescales. Different statistical measures based on the Correlation Coefficient (R), Mean Bias (MB), Root-Mean-Square Error (RMSE), and Nash–Sutcliffe efficiency (NSE) were adopted to determine the performance of both MERRA-2 datasets. The results revealed that gauge calibrated (MERRA-C) underestimated, whereas model-only (MERRA-NC) overestimated the observed seasonal cycle of precipitation. However, both datasets were able to reproduce seasonal precipitation cycle with a high correlation (R ≥ 0.95), as revealed by observation. MERRA-C datasets showed a more consistent spatial performance (higher R-value) to the observed datasets than MERRA-NC, while MERRA-NC is more reasonable to estimate precipitation amount (lower MB) across the country. Both MERRA-2 datasets performed better in winter, post-monsoon, and pre-monsoon than in summer monsoon. Moreover, MERRA-NC overestimated the observed precipitation in mid and high-elevation areas, whereas MERRA-C severely underestimated at most of the stations throughout all seasons. Among both datasets, MERRA-C was only able to reproduce the observed elevation dependency pattern. Furthermore, uncertainties in MERRA-2 precipitation products mentioned above are still worthy of attention by data developers and users.

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 The Contribution of Rain Gauges in the Calibration of the IMERG Product: Results from the First Validation over Spain

2020 ◽  
Vol 21 (2) ◽  
pp. 161-182 ◽  
Author(s):  
Francisco J. Tapiador ◽  
Andrés Navarro ◽  
Eduardo García-Ortega ◽  
Andrés Merino ◽  
José Luis Sánchez ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Surface Precipitation ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Level 3 ◽  
Hydrologic Impacts ◽  
A Minor ◽  
Global Precipitation Measurement ◽  
Global Precipitation

AbstractAfter 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough quality-controlled data to allow the first validation of their precipitation estimates over Spain. High-quality gauge data from the meteorological network of the Spanish Meteorological Agency (AEMET) are used here to validate Integrated Multisatellite Retrievals for GPM (IMERG) level 3 estimates of surface precipitation. While aggregated values compare notably well, some differences are found in specific locations. The research investigates the sources of these discrepancies, which are found to be primarily related to the underestimation of orographic precipitation in the IMERG satellite products, as well as to the number of available gauges in the GPCC gauges used for calibrating IMERG. It is shown that IMERG provides suboptimal performance in poorly instrumented areas but that the estimate improves greatly when at least one rain gauge is available for the calibration process. A main, generally applicable conclusion from this research is that the IMERG satellite-derived estimates of precipitation are more useful (r2 > 0.80) for hydrology than interpolated fields of rain gauge measurements when at least one gauge is available for calibrating the satellite product. If no rain gauges were used, the results are still useful but with decreased mean performance (r2 ≈ 0.65). Such figures, however, are greatly improved if no coastal areas are included in the comparison. Removing them is a minor issue in terms of hydrologic impacts, as most rivers in Spain have their sources far from the coast.

scholarly journals First-Year Evaluation of GPM Rainfall over the Netherlands: IMERG Day 1 Final Run (V03D)

2016 ◽  
Vol 17 (11) ◽  
pp. 2799-2814 ◽  
Author(s):  
M. F. Rios Gaona ◽  
A. Overeem ◽  
H. Leijnse ◽  
R. Uijlenhoet
Keyword(s):  
The Netherlands ◽  
Land Surface ◽  
Tropical Rainfall Measuring Mission ◽  
Ground Truth ◽  
Rain Gauge ◽  
First Year ◽  
Radar Rainfall ◽  
High Resolution Data ◽  
Precipitation Estimates ◽  
Global Precipitation

Abstract The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed.

scholarly journals Ground validation of GPM IMERG-F precipitation products with the point rain gauge records on the extreme rainfall over a mountainous area of Sumatra Island

2022 ◽  
Vol 8 (1) ◽  
pp. 163-170
Author(s):  
Ravidho Ramadhan ◽  
Marzuki Marzuki ◽  
Helmi Yusnaini ◽  
Ayu Putri Ningsih ◽  
Hiroyuki Hashiguchi ◽  
...  
Keyword(s):  
Precipitation Amount ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Precipitation Intensity ◽  
Surface Measurement ◽  
Mountainous Area ◽  
Precipitation Frequency ◽  
Sumatra Island ◽  
Ground Validation ◽  
Precipitation Estimates

Accurate satellite precipitation estimates over areas of complex topography are still challenging, while such accuracy is of importance to the adoption of satellite data for hydrological applications. This study evaluated the ability of Integrated Multi-satellitE Retrievals for GPM -Final (IMERG) V06 product to observe the extreme rainfall over a mountainous area of Sumatra Island. Fifteen years of optical rain gauge (ORG) observation at Kototabang, West Sumatra, Indonesia (100.32°E, 0.20°S, 865 m above sea level), were used as reference surface measurement. The performance of IMERG-F was evaluated using 13 extreme rain indexes formulated by the Expert Team on Climate Change Detection and Indices (ETCCDI). The IMERG-F overestimated the values of all precipitation amount-based indices (PRCPTOT, R85P, R95P, and R99P), three precipitation frequency-based indices (R1mm, R10mm, R20mm), one precipitation duration-based indices (CWD), and one precipitation intensity-based indices (RX5day). Furthermore, the IMERG-F underestimated the values of precipitation frequency-based indices (R50mm), one precipitation duration-based indices (CDD), one precipitation intensity-based indices (SDII). In terms of correlation, only five indexes have a correlation coefficient (R) > 0.5, consistent with Kling–Gupta Efficiency (KGE) value. These results confirm the need to improve the accuracy of the IMERG-F data in mountainous areas.

scholarly journals Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 2: Precipitation Rates with Elektro-L2 and Insat-3D

2020 ◽  
Vol 12 (13) ◽  
pp. 2114
Author(s):  
Christine Kolbe ◽  
Boris Thies ◽  
Nazli Turini ◽  
Zhiyu Liu ◽  
Jörg Bendix
Keyword(s):  
Tibetan Plateau ◽  
Rain Gauge ◽  
The Tibetan Plateau ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Geo Satellites ◽  
Spatio Temporal ◽  
Model Training ◽  
Global Precipitation Measurement ◽  
Global Precipitation

We present the new Precipitation REtrieval covering the TIbetan Plateau (PRETIP) as a feasibility study using the two geostationary (GEO) satellites Elektro-L2 and Insat-3D with reference to the GPM (Global Precipitation Measurement Mission) IMERG (Integrated Multi-satellitE Retrievals for GPM) product. The present study deals with the assignment of the rainfall rate. For precipitation rate assignment, the best-quality precipitation estimates from the gauge calibrated microwave (MW) within the IMERG product were combined with the GEO data by Random Forest (RF) regression. PRETIP was validated with independent MW precipitation information not considered for model training and revealed a good performance on 30 min and 11 km spatio-temporal resolution with a correlation coefficient of R = 0.59 and outperforms the validation of the independent MW precipitation with IMERG’s IR only product (R = 0.18). A comparison of PRETIP precipitation rates in 4 km resolution with daily rain gauge measurements from the Chinese Ministry of Water Resources revealed a correlation of R = 0.49. No differences in the performance of PRETIP for various elevation ranges or between the rainy (July, August) and the dry (May, September) season could be found.

scholarly journals An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed

2021 ◽  
Vol 13 (2) ◽  
pp. 234
Author(s):  
Na Zhao
Keyword(s):  
High Resolution ◽  
Model Performance ◽  
Absolute Error ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Residual Correction ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.

scholarly journals Bias Adjustment of Satellite Precipitation Estimation Using Ground-Based Measurement: A Case Study Evaluation over the Southwestern United States

2009 ◽  
Vol 10 (5) ◽  
pp. 1231-1242 ◽  
Author(s):  
Farid Ishak Boushaki ◽  
Kuo-Lin Hsu ◽  
Soroosh Sorooshian ◽  
Gi-Hyeon Park ◽  
Shayesteh Mahani ◽  
...  
Keyword(s):  
United States ◽  
Temporal Distribution ◽  
Rain Gauge ◽  
Systematic Bias ◽  
Southwestern United States ◽  
Precipitation Measurement ◽  
Crucial Component ◽  
Precipitation Estimation ◽  
Precipitation Estimates

Abstract Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN–CCS) are adjusted based on the Climate Prediction Center (CPC) daily rain gauge analysis. To remove the bias, the hourly CCS estimates were spatially and temporally accumulated to the daily 1° × 1° scale, the resolution of CPC rain gauge analysis. The daily CCS bias was then downscaled to the hourly temporal scale to correct hourly CCS estimates. The bias corrected CCS estimates are called the adjusted CCS (CCSA) product. With the adjustment from the gauge measurement, CCSA data have been generated to provide more reliable high temporal/spatial-resolution precipitation estimates. In the case study, the CCSA precipitation estimates from the proposed approach are compared against ground-based measurements in high-density gauge networks located in the southwestern United States.

scholarly journals Evaluation of Global Precipitation Measurement Rainfall Estimates against Three Dense Gauge Networks

2018 ◽  
Vol 19 (3) ◽  
pp. 517-532 ◽  
Author(s):  
Jackson Tan ◽  
Walter A. Petersen ◽  
Gottfried Kirchengast ◽  
David C. Goodrich ◽  
David B. Wolff
Keyword(s):  
Land Surface ◽  
A Priori ◽  
Passive Microwave ◽  
Dual Frequency ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Microwave Sensors ◽  
Rainfall Estimates

Abstract Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.

scholarly journals Easy-to-use spatial Random Forest-based downscaling-calibration method for producing high resolution and accurate precipitation data

2021 ◽  
Author(s):  
Chuanfa Chen ◽  
Baojian Hu ◽  
Yanyan Li
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Autocorrelation ◽  
Land Surface ◽  
Calibration Method ◽  
Rain Gauge ◽  
Important Variable ◽  
Kriging Interpolation ◽  
Slope Aspect ◽  
Precipitation Data

Abstract. High resolution and accurate precipitation data is significantly important for numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on spatial Random Forest (SRF) is proposed in this paper, where the spatial autocorrelation between precipitation measurements is taken into account. The proposed method consists of two main stages. Firstly, the satellite-based precipitation was downscaled by SRF with the incorporation of some high-resolution covariates including latitude, longitude, DEM, NDVI, terrain slope, aspect, relief, and land surface temperatures. Then, the downscaled precipitation was calibrated by SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) located in Sichuan province, China from 2015 to 2019 was processed using our method and its results were compared with those of some classical methods including geographically weighted regression (GWR), artificial neural network (ANN), random forest (RF), kriging interpolation only on gauge measurements, bilinear interpolation-based downscaling and then SRF-based calibration (Bi-SRF), and SRF-based downscaling and then geographical difference analysis (GDA)-based calibration (SRF-GDA). Results show that: (1) the proposed method outperforms the other methods as well as the original IMERG; (2) the monthly-based SRF estimation is slightly more accurate than the annual-based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration preforms better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging seems more accurate than GWR and ANN in terms of quantitative accuracy measures, whereas its precipitation map cannot capture the detailed spatial precipitation patterns; and (5) among the predictors for calibration, the precipitation interpolated by kriging on the gauge measurements is the most important variable, indicating the significance for the inclusion of spatial autocorrelation information in gauge measurements.

scholarly journals Evaluation of GPM IMERG Early, Late, and Final rainfall estimates with WegenerNet gauge data in southeast Austria

2017 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche ◽  
Gottfried Kirchengast ◽  
Jürgen Fuchsberger ◽  
Jackson Tan ◽  
...  
Keyword(s):  
Real Time ◽  
Rain Gauge ◽  
Time Data ◽  
Precipitation Measurement ◽  
Hourly Rainfall ◽  
Precipitation Estimates ◽  
Climate Station ◽  
Global Precipitation ◽  
Early Late ◽  
Rainfall Estimates

Abstract. The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) products provide quasi-global (60° N-60° S) precipitation estimates, beginning March 2014, from the combined use of passive microwave (PMW) and infrared (IR) satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e. IMERG Early and Late, and of post-real-time research data, i.e. IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG Early, Late, and Final (IMERG-E, -L, and -F) half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach Region (WEGN) high density climate station network in southeast Austria. The comparison is conducted over two IMERG 0.1° x 0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, with data during the extended summer season (April–October) for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high) and two seasons (warm and hot), and we evaluate the performance of IMERG using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villariniand Krajewski (2007), we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25-min gauge accumulation, with an offset of +40 min relative to its nominal time.

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