scholarly journals Evaluation of High-Resolution Multisatellite and Reanalysis Rainfall Products over East Africa

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Dejene Sahlu ◽  
Semu A. Moges ◽  
Efthymios I. Nikolopoulos ◽  
Emmanouil N. Anagnostou ◽  
Dereje Hailu
Keyword(s):  
Rainfall Intensity ◽  
Threshold Values ◽  
Wet Season ◽  
Error Metrics ◽  
Blue Nile Basin ◽  
Precipitation Estimates ◽  
Error Metric ◽  
Satellite Estimate ◽  
Better Than ◽  
Daily Time

The performance of six satellite-based and three newly released reanalysis rainfall estimates are evaluated at daily time scale and spatial grid size of 0.25 degrees during the period of 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, with the view of improving the reliability of precipitation estimates of the wet (June to September) and secondary rainy (March to May) seasons. The study evaluated both adjusted and unadjusted satellite-based products of TMPA, CMORPH, PERSIANN, and ECMWF ERA-Interim reanalysis as well as Multi-Source Weighted-Ensemble Precipitation (MSWEP) estimates. Among the six satellite-based rainfall products, adjusted CMORPH exhibits the best accuracy of the wet season rainfall estimate. In the secondary rainy season, unadjusted CMORPH and 3B42V7 are nearly equivalent in terms of bias, POD, and CSI error metrics. All error metric statistics show that MSWEP outperform both unadjusted and gauge adjusted ERA-Interim estimates. The magnitude of error metrics is linearly increasing with increasing percentile threshold values of gauge rainfall categories. Overall, all precipitation datasets need further improvement in terms of detection during the occurrence of high rainfall intensity. MSWEP detects higher percentiles values better than satellite estimate in the wet and poor in the secondary rainy seasons.

scholarly journals Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 687
Author(s):  
Salman Sakib ◽  
Dawit Ghebreyesus ◽  
Hatim O. Sharif
Keyword(s):  
Stage Iv ◽  
Tropical Storm ◽  
Average Correlation ◽  
Acceptable Range ◽  
Precipitation Estimates ◽  
Overall Performance ◽  
Early Late ◽  
Statistical Metrics ◽  
Better Than

Tropical Storm Imelda struck the southeast coastal regions of Texas from 17–19 September, 2019, and delivered precipitation above 500 mm over about 6000 km2. The performance of the three IMERG (Early-, Late-, and Final-run) GPM satellite-based precipitation products was evaluated against Stage-IV radar precipitation estimates. Basic and probabilistic statistical metrics, such as CC, RSME, RBIAS, POD, FAR, CSI, and PSS were employed to assess the performance of the IMERG products. The products captured the event adequately, with a fairly high POD value of 0.9. The best product (Early-run) showed an average correlation coefficient of 0.60. The algorithm used to produce the Final-run improved the quality of the data by removing systematic errors that occurred in the near-real-time products. Less than 5 mm RMSE error was experienced in over three-quarters (ranging from 73% to 76%) of the area by all three IMERG products in estimating the Tropical Storm Imelda. The Early-run product showed a much better RBIAS relatively to the Final-run product. The overall performance was poor, as areas with an acceptable range of RBIAS (i.e., between −10% and 10%) in all the three IMERG products were only 16% to 17% of the total area. Overall, the Early-run product was found to be better than Late- and Final-run.

scholarly journals Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

2021 ◽  
Vol 13 (2) ◽  
pp. 254 ◽  
Author(s):  
Jie Hsu ◽  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Xiuzhen Li
Keyword(s):  
Annual Cycle ◽  
Interannual Variation ◽  
Winter Precipitation ◽  
Monthly Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Variation ◽  
Global Precipitation ◽  
Multiple Timescale ◽  
Better Than

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.

scholarly journals Impact of Topography and Rainfall Intensity on the Accuracy of IMERG Precipitation Estimates in an Arid Region

2020 ◽  
Vol 13 (1) ◽  
pp. 13
Author(s):  
Mohammed T. Mahmoud ◽  
Safa A. Mohammed ◽  
Mohamed A. Hamouda ◽  
Mohamed M. Mohamed
Keyword(s):  
Rainfall Intensity ◽  
Poor Performance ◽  
Rain Gauge ◽  
Detection Accuracy ◽  
Rain Gauge Data ◽  
Light Rain ◽  
Mountainous Regions ◽  
Data Gaps ◽  
Precipitation Estimates ◽  
Evaluation Techniques

The influence of topographical characteristics and rainfall intensity on the accuracy of satellite precipitation estimates is of importance to the adoption of satellite data for hydrological applications. This study evaluates the three GPM IMERG V05B products over the arid country of Saudi Arabia. Statistical indices quantifying the performance of IMERG products were calculated under three evaluation techniques: seasonal-based, topographical, and rainfall intensity-based. Results indicated that IMERG products have the capability to detect seasons with the highest precipitation values (spring) and seasons with the lowest precipitation (summer). Moreover, results showed that IMERG products performed well under various rainfall intensities, particularly under light rain, which is the most common rainfall in arid regions. Furthermore, IMERG products exhibited high detection accuracy over moderate elevations, whereas it had poor performance over coastal and mountainous regions. Overall, the results confirmed that the performance of the final-run product surpassed the near-real-time products in terms of consistency and errors. IMERG products can improve temporal resolution and play a significant role in filling data gaps in poorly gauged regions. However, due to the errors in IMERG products, it is recommended to use sub-daily rain gauge data in satellite calibration for better rainfall estimation over arid and semiarid regions.

scholarly journals First Evaluation of the Day-1 IMERG over the Upper Blue Nile Basin

2016 ◽  
Vol 17 (11) ◽  
pp. 2875-2882 ◽  
Author(s):  
Dejene Sahlu ◽  
Efthymios I. Nikolopoulos ◽  
Semu A. Moges ◽  
Emmanouil N. Anagnostou ◽  
Dereje Hailu
Keyword(s):  
Time Scales ◽  
Spatial Scales ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Nile Basin ◽  
Wet Season ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Hourly Rainfall ◽  
Upper Blue Nile Basin

Abstract This work presents a first evaluation of the performance of the Integrated Multisatellite Retrievals for GPM (IMERG) precipitation product over the upper Blue Nile basin of Ethiopia. One of the unique features of this study is the availability of hourly rainfall measurements from an experimental rain gauge network in the area. Both the uncalibrated and calibrated versions of IMERG are evaluated, and their performance is contrasted against another high-resolution satellite product, which is the Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH). The analysis is performed for hourly and daily time scales and at spatial scales that correspond to the nominal resolution of satellite products, which is 0.1° spatial resolution. The period analyzed is focused on a single wet season (May–October 2014). Evaluation is performed using several statistical and categorical error metrics, as well as spatial correlation analysis to assess the ability of satellite products to represent spatial variability of precipitation in the area. Results show that both IMERG products have a better bias ratio and correlation coefficient on both time scales as compared to CMORPH. Comparison statistics show a slight improvement in the skill of detecting rainfall events in IMERG products compared to CMORPH. Results also show a decreasing trend in the detection ability of satellite products for increasing threshold values, highlighting the need to further improve detection during heavy precipitation.

scholarly journals Assessing the Uncertainties of Four Precipitation Products for Swat Modeling in Mekong River Basin

2019 ◽  
Vol 11 (3) ◽  
pp. 304 ◽  
Author(s):  
Xiongpeng Tang ◽  
Jianyun Zhang ◽  
Chao Gao ◽  
Gebdang Ruben ◽  
Guoqing Wang
Keyword(s):  
River Basin ◽  
Assessment Tool ◽  
Model Performance ◽  
Mekong River ◽  
Distributed Hydrological Model ◽  
Parameter Uncertainties ◽  
Mekong River Basin ◽  
Daily Streamflow ◽  
Precipitation Estimates ◽  
Better Than

Using hydrological simulation to evaluate the accuracy of satellite-based and reanalysis precipitation products always suffer from a large uncertainty. This study evaluates four widely used global precipitation products with high spatial and temporal resolutions [i.e., AgMERRA (AgMIP modern-Era Retrospective Analysis for Research and Applications), MSWEP (Multi-Source Weighted-Ensemble Precipitation), PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record), and TMPA (Tropical Rainfall Measuring Mission 3B42 Version7)] against gauge observations with six statistical metrics over Mekong River Basin (MRB). Furthermore, the Soil and Water Assessment Tool (SWAT), a widely used semi-distributed hydrological model, is calibrated using different precipitation inputs. Both model performance and uncertainties of parameters and prediction have been quantified. The following findings were obtained: (1) The MSWEP and TMPA precipitation products have good accuracy with higher CC, POD, and lower ME and RMSE, and the AgMERRA precipitation estimates perform better than PERSIANN-CDR in this rank; and (2) out of the six different climate regions of MRB, all six metrics are worse than that in the whole MRB. The AgMERRA can better reproduce the occurrence and contributions at different precipitation densities, and the MSWEP has the best performance in Cwb, Cwa, Aw, and Am regions that belong to the low latitudes. (3) Daily streamflow predictions obtained using MSWEP precipitation estimates are better than those simulated by other three products in term of both the model performance and parameter uncertainties; and (4) although MSWEP better captures the precipitation at different intensities in different climatic regions, the performance can still be improved, especially in the regions with higher altitude.

scholarly journals On the Uncertainty of High-Resolution Hourly Quantitative Precipitation Estimates in California

2020 ◽  
Vol 21 (5) ◽  
pp. 865-879
Author(s):  
Janice L. Bytheway ◽  
Mimi Hughes ◽  
Kelly Mahoney ◽  
Rob Cifelli
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Heavy Rain ◽  
Reference Dataset ◽  
Wet Season ◽  
Large Variability ◽  
Operational Forecast ◽  
Bay Area ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

AbstractThe Bay Area of California and surrounding region receives much of its annual precipitation during the October–March wet season, when atmospheric river events bring periods of heavy rain that challenge water managers and may exceed the capacity of storm sewer systems. The complex terrain of this region further complicates the situation, with terrain interactions that are not currently captured in most operational forecast models and inadequate precipitation measurements to capture the large variability throughout the area. To improve monitoring and prediction of these events at spatial and temporal resolutions of interest to area water managers, the Bay Area Advanced Quantitative Precipitation Information project was developed. To quantify improvements in forecast precipitation, model validation studies require a reference dataset to compare against. In this paper we examine 10 gridded, high-resolution (≤10 km, hourly) precipitation estimates to assess the uncertainty of high-resolution quantitative precipitation estimates (QPE) in areas of complex terrain. The products were linearly interpolated to 3-km grid spacing, which is the resolution of the operational forecast model to be validated. Substantial differences exist between the various products at accumulation periods ranging from hourly to annual, with standard deviations among the products exceeding 100% of the mean. While the products seem to agree fairly well on the timing of precipitation, intensity estimates differ, sometimes by an order of magnitude. The results highlight both the need for additional observations and the need to account for uncertainty in the reference dataset when validating forecasts in this area.

scholarly journals Evaluation of Satellite Precipitation Estimates over Australia

2020 ◽  
Vol 12 (4) ◽  
pp. 678 ◽  
Author(s):  
Zhi-Weng Chua ◽  
Yuriy Kuleshov ◽  
Andrew Watkins
Keyword(s):  
Rainfall Intensity ◽  
Austral Summer ◽  
Rain Gauge ◽  
Reference Dataset ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Notable Increase ◽  
Climate Prediction Center

This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.

scholarly journals Comparison of Global Precipitation Estimates across a Range of Temporal and Spatial Scales

2016 ◽  
Vol 29 (21) ◽  
pp. 7773-7795 ◽  
Author(s):  
Maria Gehne ◽  
Thomas M. Hamill ◽  
George N. Kiladis ◽  
Kevin E. Trenberth
Keyword(s):  
Time Scales ◽  
Large Scale ◽  
Global Climate ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Random Errors ◽  
Climate Data ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Daily Time

Abstract Characteristics of precipitation estimates for rate and amount from three global high-resolution precipitation products (HRPPs), four global climate data records (CDRs), and four reanalyses are compared. All datasets considered have at least daily temporal resolution. Estimates of global precipitation differ widely from one product to the next, with some differences likely due to differing goals in producing the estimates. HRPPs are intended to produce the best snapshot of the precipitation estimate locally. CDRs of precipitation emphasize homogeneity over instantaneous accuracy. Precipitation estimates from global reanalyses are dynamically consistent with the large-scale circulation but tend to compare poorly to rain gauge estimates since they are forecast by the reanalysis system and precipitation is not assimilated. Regional differences among the estimates in the means and variances are as large as the means and variances, respectively. Even with similar monthly totals, precipitation rates vary significantly among the estimates. Temporal correlations among datasets are large at annual and daily time scales, suggesting that compensating bias errors at annual and random errors at daily time scales dominate the differences. However, the signal-to-noise ratio at intermediate (monthly) time scales can be large enough to result in high correlations overall. It is shown that differences on annual time scales and continental regions are around 0.8 mm day−1, which corresponds to 23 W m−2. These wide variations in the estimates, even for global averages, highlight the need for better constrained precipitation products in the future.

scholarly journals Selecting models for the estimation of reference evapotranspiration for irrigation scheduling purposes

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245270
Author(s):  
Lucas Borges Ferreira ◽  
Fernando França da Cunha ◽  
Sidney Sara Zanetti
Keyword(s):  
Root Mean Square Error ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Irrigation Scheduling ◽  
Soil Water Balance ◽  
Suitable Model ◽  
Mean Square ◽  
Error Metrics ◽  
Performance Gains ◽  
Better Than

Alternative models for the estimation of reference evapotranspiration (ETo) are typically assessed using traditional error metrics, such as root mean square error (RMSE), which may not be sufficient to select the best model for irrigation scheduling purposes. Thus, this study analyzes the performance of the original and calibrated Hargreaves-Samani (HS), Romanenko (ROM) and Jensen-Haise (JH) equations, initially assessed using traditional error metrics, for use in irrigation scheduling, considering the simulation of different irrigation intervals/time scales. Irrigation scheduling was simulated using meteorological data collected in Viçosa-MG and Mocambinho-MG, Brazil. The Penman-Monteith FAO-56 equation was used as benchmark. In general, the original equations did not perform well to estimate ETo, except the ROM and HS equations used at Viçosa and Mocambinho, respectively. Calibration and the increase in the time scale provided performance gains. When applied in irrigation scheduling, the calibrated HS and JH equations showed the best performances. Even with greater errors in estimating ETo, the calibrated HS equation performed similarly or better than the calibrated JH equation, as it had errors with greater potential to be canceled during the soil water balance. Finally, in addition to using error metrics, the performance of the models throughout the year should be considered in their assessment. Furthermore, simulating the application of ETo models in irrigation scheduling can provide valuable information for choosing the most suitable model.

scholarly journals Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel

2017 ◽  
Author(s):  
Wenmin Zhang ◽  
Martin Brandt ◽  
Xiaoye Tong ◽  
Qingjiu Tian ◽  
Rasmus Fensholt
Keyword(s):  
Rainfall Intensity ◽  
Arid Zone ◽  
Growing Season ◽  
Seasonal Distribution ◽  
Seasonal Rainfall ◽  
Wet Season ◽  
Rainfall Events ◽  
Heavy Rainfall Events ◽  
Semi Arid ◽  
Rainy Days

Abstract. Climate change in drylands has caused alterations in the seasonal distribution of rainfall including increased heavy rainfall events, longer dry spells, and a shifted timing of the wet season. Yet, the aboveground net primary productivity (ANPP) in drylands is usually explained by annual rainfall sums, disregarding the influence of the seasonal distribution of rainfall. This study tests the importance of seasonal rainfall metrics (onset and cessation of the wet season, number of rainy days, rainfall intensity, number of consecutive dry days and heavy rainfall events) on growing season ANPP. We focus on the Sahel and north-Sudanian region (100–800 mm year−1) and apply daily satellite based rainfall estimates (RFE-2.0) and growing season integrated NDVI (MODIS) as a proxy for ANNP over the study period 2001–2015. Growing season ANPP in the arid zone (100–300 mm year−1) was found to be rather insensitive to variations in the seasonal rainfall metrics, whereas vegetation in the semi-arid zone (300–700 mm year−1) was significantly impacted by most metrics, especially by the number of rainy days and timing (start and cessation) of the wet season. We analyzed critical breakpoints for all metrics, showing that growing season ANPP is particularly negatively impacted after > 10 consecutive dry days and that a rainfall intensity of 7 mm day−1 is detected for optimum growing season ANPP. We conclude that number of rainy days and the timing of the wet season are seasonal rainfall metrics being decisive for favorable vegetation growth in semi-arid Sahel which needs to be considered when modelling primary productivity from rainfall in the dryland's of Sahel and elsewhere.

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