scholarly journals Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

2019 ◽  
Vol 11 (23) ◽  
pp. 2741 ◽  
Author(s):  
Aminyavari ◽  
Saghafian ◽  
Sharifi
Keyword(s):  
Weather Prediction ◽  
Precipitation Amount ◽  
Early Warning Systems ◽  
Flood Impacts ◽  
Spatial Coverage ◽  
Basin Scale ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Threshold ◽  
Precipitation Thresholds

Precipitation monitoring and early warning systems are required to reduce negative flood impacts. In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the THORPEX interactive grand global ensemble (TIGGE) as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG, for precipitation estimates were evaluated in recent severe floods in Iran over the March–April 2019 period. The evaluations were conducted in three aspects: spatial distribution of precipitation, mean areal precipitation in three major basins hard hit by the floods, and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Moreover, with regard to mean precipitation at the basin scale, UKMO and European Center for Medium-Range Weather Forecasts (ECMWF) models in the Gorganrud Basin, ECMWF in the Karkheh Basin and UKMO in the Karun Basin performed better than others in flood forecasting. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the ECMWF had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models.

Performance evaluation of ensemble precipitation forecasts and satellite products for the spring 2019 severe floods in Iran

2021 ◽  
Author(s):  
Saleh Aminyavari ◽  
Bahram Saghafian ◽  
Ehsan Sharifi
Keyword(s):  
Spatial Distribution ◽  
Weather Prediction ◽  
Precipitation Amount ◽  
Warning System ◽  
Extreme Precipitation Events ◽  
Spatial Coverage ◽  
Precipitation Estimates ◽  
Precipitation Threshold ◽  
Precipitation Thresholds ◽  
Precipitation Events

<p>In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the TIGGE database as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG-RT V05B, for precipitation estimates were evaluated in recent severe floods in Iran over the March–April 2019 period. The evaluations were conducted in two modes: spatial distribution of precipitation and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Although, generally, the models captured the spatial distribution of heavy precipitation events, the hot spots were not located in the correct area. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the Medium-Range Weather Forecasts (ECMWF) had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models. Although, the models and IMERG product underestimated or overestimated the amount of precipitation, but they were able to detect most extreme precipitation events. Overall, the results of this study show the IMERG precipitation estimates and NWP ensemble forecasts performed well in the three major flood events in spring 2019 in Iran. Given wide spread damages caused by the floods, the necessity of establishing an efficient flood warning system using the best precipitation products is advised.</p><p> </p>

scholarly journals Evaluation of Precipitation Estimation Accuracy in Reanalyses, Satellite Products, and an Ensemble Method for Regions in Australia and South and East Asia

2013 ◽  
Vol 14 (4) ◽  
pp. 1323-1333 ◽  
Author(s):  
Jorge L. Peña-Arancibia ◽  
Albert I. J. M. van Dijk ◽  
Luigi J. Renzullo ◽  
Mark Mulligan
Keyword(s):  
East Asia ◽  
Precipitation Amount ◽  
Department Of Energy ◽  
Estimation Accuracy ◽  
Monthly Precipitation ◽  
Precipitation Regime ◽  
Monsoon Period ◽  
Ensemble Mean ◽  
Precipitation Estimation ◽  
Precipitation Estimates

Abstract Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologic applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australia (SILO) and South and East Asia [Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] to calculate the daily detection and accuracy metrics for three reanalyses [ECMWF Re-Analysis Interim (ERA-Interim), Japanese 25-yr Reanalysis (JRA-25), and NCEP–Department of Energy (DOE) Global Reanalysis 2] and three satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) 3B42V6, Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)]. A depth-frequency-adjusted ensemble mean of the reanalyses and satellite products was also evaluated. Reanalyses precipitation from ERA-Interim in southern Australia (SAu) and northern Australasia (NAu) showed higher detection performance. JRA-25 had a better performance in South and East Asia (SEA) except for the monsoon period, in which satellite estimates from TRMM and CMORPH outperformed the reanalyses. In terms of accuracy metrics (correlation coefficient, root-mean-square difference, and a precipitation intensity proxy, which is the ratio of monthly precipitation amount to total days with precipitation) and over the three subdomains, the depth-frequency-adjusted ensemble mean generally outperformed or was nearly as good as any of the single members. The results of the ensemble show that additional information is captured from the different precipitation products. This finding suggests that, depending on precipitation regime and location, combining (re)analysis and satellite products can lead to better precipitation estimates and, thus, more accurate hydrological applications than selecting any single product.

scholarly journals Evaluation of Radar Precipitation Estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the Conterminous United States

2012 ◽  
Vol 13 (3) ◽  
pp. 1080-1093 ◽  
Author(s):  
Wanru Wu ◽  
David Kitzmiller ◽  
Shaorong Wu
Keyword(s):  
United States ◽  
Precipitation Amount ◽  
Processing System ◽  
Warm Season ◽  
Distribution Data ◽  
Time Interval ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Estimation System

Abstract This study evaluated 24-, 6-, and 1-h radar precipitation estimated from the National Mosaic and Multisensor Quantitative Precipitation Estimation System (NMQ) and the Weather Surveillance Radar-1988 Doppler (WSR-88D) Precipitation Processing System (PPS) over the conterminous United States (CONUS) for the warm season April–September 2009 and the cool season October 2009–March 2010. Precipitation gauge observations from the Automated Surface Observing System (ASOS) were used as the ground truth. Gridded StageIV multisensor precipitation estimates were applied for supplementary verification. The comparison of the two systems consisted of a series of analyses including the linear correlation coefficient (CC) and the root-mean-square error (RMSE) between the radar precipitation estimates and the gauge observations, large precipitation amount detection categorical scores, and the reliability of precipitation amount distribution. Data stratified for the 12 CONUS River Forecast Centers (RFCs) and for the cold rains events with bright-band effects were analyzed additionally. Major results are 1) the linear CC of NMQ versus ASOS are generally higher than that of PPS versus ASOS over CONUS, while the spatial variations stratified by the RFCs may switch with seasons; 2) compared to the precipitation distribution of ASOS, NMQ shows less deviation than PPS; 3) for the cold rains verified against ASOS, NMQ has higher CC and PPS has lower RMSE for 6-h and higher RMSE for 1-h cold rains; and 4) for the precipitation detection categorical scores, either NMQ or PPS can be superior, depending on the time interval and season. The verification against StageIV gridded precipitation estimates showed that NMQ consistently had higher correlations and lower biases than did PPS.

scholarly journals Simulation of rainfall fields conditioned on rain gauge observations and radar estimates using random mixing

2021 ◽  
Author(s):  
Jieru Yan ◽  
Fei Li ◽  
András Bárdossy ◽  
Tao Tao
Keyword(s):  
Distribution Function ◽  
Marginal Distribution ◽  
Conditional Simulation ◽  
Simulation Method ◽  
Rain Gauge ◽  
Spatial Coverage ◽  
Two Factors ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Random Mixing

Abstract. The accuracy of spatial precipitation estimates with the relatively high temporospatial resolution is of vital importance in various fields of research and practice. Yet the intricate variability and the intermittent nature of precipitation make it very difficult to obtain accurate spatial precipitation estimates. Radar and rain gauge are two complementary sources of precipitation information: the former is inaccurate in general but is a valid indicator for the spatial pattern of the rainfall field; the latter is relatively accurate but lack spatial coverage. Considering the pros and cons of the two sources of precipitation information, a number of radar-gauge merging techniques have been developed to obtain spatial precipitation estimates over the past years. Conditional simulation has great potential to be used in spatial precipitation estimation. Unlike the commonly used interpolation methods, the results from the conditional simulation is a range of possible estimates due to its Monte Carlo framework. Yet an obstacle that hampers the application of conditional simulation in spatial precipitation estimation is how to obtain the marginal distribution function of the rainfall field with accuracy. In this work, we propose a method to obtain the marginal distribution function of the rainfall field based on rain gauge observations and radar estimates. The conditional simulation method, random mixing (RM), is used to simulate rainfall fields. The properties of the results from the proposed method are elaborated through the comparison with the results from other methods: ordinary kriging, kriging with external drift, and conditional merging. Finally, the sensitivity of the proposed method towards the two factors – density of rain gauges and random error in radar estimates – is analyzed.

scholarly journals Evaluation of IMERG-E Precipitation Estimates for Fire Weather Applications in Alaska

2020 ◽  
Vol 35 (5) ◽  
pp. 1831-1843 ◽  
Author(s):  
Taylor A. Gowan ◽  
John D. Horel
Keyword(s):  
Situational Awareness ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Fire Weather ◽  
Spatial Coverage ◽  
Precipitation Estimates ◽  
Grid Points ◽  
Meteorological Observations ◽  
Forest Fire Danger

AbstractLarge wildfire outbreaks in Alaska are common from June to August. The Canadian Forest Fire Danger Rating System (CFFDRS) is used operationally by Alaskan fire managers to produce statewide fire weather outlooks and forecast guidance near active wildfires. The CFFDRS estimates of fire potential and behavior rely heavily on meteorological observations (precipitation, temperature, wind speed, and relative humidity) from the relatively small number of in situ stations across Alaska with precipitation being the most critical parameter. To improve the spatial coverage of precipitation estimates across Alaska for fire weather applications, a multisatellite precipitation algorithm was evaluated during six fire seasons (1 June–31 August 2014–19). Near-real-time daily precipitation estimates from the Integrated Multisatellite Retrievals for the Global Precipitation Mission (IMERG) algorithm were verified using 322 in situ stations across four Alaskan regions. For each region, empirical cumulative distributions of daily precipitation were obtained from station observations during each summer, and compared to corresponding distributions of interpolated values from IMERG grid points (0.1° × 0.1° grid). The cumulative distributions obtained from IMERG exhibited wet biases relative to the observed distributions for all regions, precipitation amount ranges, and summers. A bias correction approach using regional quantile mapping was developed to mitigate for the IMERG wet bias. The bias-adjusted IMERG daily precipitation estimates were then evaluated and found to produce improved gridded IMERG precipitation estimates. This approach may help to improve situational awareness of wildfire potential across Alaska and be appropriate for other high-latitude regions where there are sufficient in situ precipitation observations to help correct the IMERG precipitation estimates.

scholarly journals Evaluating a mesoscale atmosphere model and a satellite-based algorithm in estimating extreme rainfall events in northwestern Turkey

2014 ◽  
Vol 14 (3) ◽  
pp. 611-624 ◽  
Author(s):  
I. Yucel ◽  
A. Onen
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Wrf Model ◽  
Early Warning Systems ◽  
Hydrologic Model ◽  
Rainfall Events ◽  
Extreme Precipitation Events ◽  
Precipitation Estimates ◽  
Heavy Rainfall Events

Abstract. Quantitative precipitation estimates are obtained with more uncertainty under the influence of changing climate variability and complex topography from numerical weather prediction (NWP) models. On the other hand, hydrologic model simulations depend heavily on the availability of reliable precipitation estimates. Difficulties in estimating precipitation impose an important limitation on the possibility and reliability of hydrologic forecasting and early warning systems. This study examines the performance of the Weather Research and Forecasting (WRF) model and the Multi Precipitation Estimates (MPE) algorithm in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in the western Black Sea region of Turkey. Precipitation derived from WRF model with and without the three-dimensional variational (3DVAR) data assimilation scheme and MPE algorithm at high spatial resolution (5 km) are compared with gauge precipitation. WRF-derived precipitation showed capabilities in capturing the timing of precipitation extremes and to some extent the spatial distribution and magnitude of the heavy rainfall events, whereas MPE showed relatively weak skills in these aspects. WRF skills in estimating such precipitation characteristics are enhanced with the application of the 3DVAR scheme. Direct impact of data assimilation on WRF precipitation reached up to 12% and at some points there is a quantitative match for heavy rainfall events, which are critical for hydrological forecasts.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

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 Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

2019 ◽  
Vol 20 (12) ◽  
pp. 2347-2365 ◽  
Author(s):  
Ali Jozaghi ◽  
Mohammad Nabatian ◽  
Seongjin Noh ◽  
Dong-Jun Seo ◽  
Lin Tang ◽  
...  
Keyword(s):  
Computational Cost ◽  
Mean Field ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Radar Satellite ◽  
Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

Farmer’s Drought Response and Coping Strategies at Household Level in Southern Tigray, Ethiopia

2021 ◽  
Author(s):  
Eskinder Gidey ◽  
Tirhas Gebregergis ◽  
Woldegebrial Zeweld ◽  
Haftamu Gebretsadik ◽  
Ogaile Dikinya ◽  
...  
Keyword(s):  
Coping Strategies ◽  
Water Conservation ◽  
Smallholder Farmers ◽  
Weather Prediction ◽  
Multinomial Logit Model ◽  
Early Warning Systems ◽  
Drought Severity ◽  
Severe Drought ◽  
Household Level ◽  
Household Heads

Abstract Background Drought is one of the most damaging climate-induced threats impacting the lives of many people every year. The purpose of this study was to determine farmer’s drought coping strategies both proactive and reactive responses at household level based on the field survey in Raya Azebo and Raya Chercher districts, southern Tigray, Ethiopia. Agro–climatological based 246 households were sampled from the lowlands (36), midlands (202) and highlands (8). Multinomial logit model was used to identify best drought coping strategies. Results about 24.8% of female headed and 75.2% of male headed respondents have experienced mild to extremely severe drought in the last three decades. A significant association between the various drought severity and household heads (chi–square = 9.861, df = 3, p–value < 0.05) observed. Conclusions this study concluded that collection and saving of pasture, soil and water conservation practices, and use of weather prediction information to adjust saving and farming system are best proactive drought coping strategies. Whereas, feeding of roasted cactus for livestock, borrowing loans for running small business, selling of household assets and reduction of food consumptions are the major reactive or off–farm drought coping strategies in the study area. If the responses of smallholder farmers are not well supported by the concerned bodies, the existing disaster preparedness and early warning systems in the area might be significantly affected and its impacts will be very serious on both the livelihood of local people and natural resources in the area.

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