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

Spatial Coverage ◽

Precipitation Estimates ◽

Precipitation Threshold ◽

Precipitation Thresholds ◽

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&#8211;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.&#160;

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

Remote Sensing ◽

10.3390/rs11232741 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2741 ◽

Cited By ~ 3

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.

Short-Range Numerical Weather Prediction of Extreme Precipitation Events Using Enhanced Surface Data Assimilation

Atmosphere ◽

10.3390/atmos10100587 ◽

2019 ◽

Vol 10 (10) ◽

pp. 587

Author(s):

Magnus Lindskog ◽

Tomas Landelius

Keyword(s):

Data Assimilation ◽

Numerical Weather Prediction ◽

Short Range ◽

Weather Prediction ◽

Heavy Precipitation ◽

Numerical Weather ◽

Surface Data ◽

Enhanced Surface ◽

A limited-area kilometre scale numerical weather prediction system is applied to evaluate the effect of refined surface data assimilation on short-range heavy precipitation forecasts. The refinements include a spatially dependent background error representation, use of a flow-dependent data assimilation technique, and use of data from a satellite-based scatterometer instrument. The effect of the enhancements on short-term prediction of intense precipitation events is confirmed through a number of case studies. Verification scores and subjective evaluation of one particular case points at a clear impact of the enhanced surface data assimilation on short-range heavy precipitation forecasts and suggest that it also tends to slightly improve them. Although this is not strictly statistically demonstrated, it is consistent with the expectation that a better surface state should improve rainfall forecasts.

Are precipitation anomalies associated with aerosol variations over Eastern China?

10.5194/acp-2016-1034 ◽

2017 ◽

Author(s):

Xiangde Xu ◽

Xueliang Guo ◽

Tianliang Zhao ◽

Xingqin An ◽

Yang Zhao ◽

...

Keyword(s):

Spatial Distribution ◽

Interannual Variability ◽

Eastern China ◽

Interdecadal Variation ◽

Aerosol Concentration ◽

Atmospheric Environment ◽

Western China ◽

Light Rain ◽

Abstract. In Eastern China (EC), strong anthropogenic emissions deteriorate the atmospheric environment harbored by the upstream Tibetan and Loess Plateaus, building a south-north zonal distribution of high anthropogenic aerosols. This research analyzed the interannual variability of precipitations with different intensities in the EC region from 1961 to 2010. We found that the frequency of light rain significantly decreased and the occurrence of rainstorm, especially the extraordinary rainstorm significantly increased over the recent decades. The extreme precipitation events presented the same interannual variability pattern with the frequent haze events. Moreover, the extreme rainfall events of various intensities showed a regular interannual variability trend. During the 1980s, the regional precipitation trends in EC showed an obvious "transform" from more light rain to more extreme rainstorms. The running correlation analysis of interdecadal variation further verified that the correlation between the increasing aerosol emissions and the frequency of abnormal precipitation events tended to be more significant in the EC. The correlation between atmospheric visibility and low cloud amounts, which are both closely related with aerosol concentrations, had a spatial distribution of "northern positive and southern negative" pattern, and the spatial distribution of the frequency variability of regional rainstorms was "southern positive and northern negative". After the 1990s, the visibility in summer season deteriorated more remarkably than other seasons, and the light rain frequency decreased obviously while the rainstorm and extraordinary heavy rainfall occurred more frequently. There were significant differences in the interdecadal variation trends in light rain and rainstorm events between the high aerosol concentration areas in the EC and the relatively "clean area" in western China. The aircraft measurements over the EC confirmed that the diameters of cloud droplets decreased under high aerosol concentration condition, thereby inhibiting weak precipitation process.

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

Remote Sensing ◽

10.3390/rs12111836 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1836 ◽

Cited By ~ 8

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.

Multi-Indicator Evaluation for Extreme Precipitation Events in the Past 60 Years over the Loess Plateau

Water ◽

10.3390/w12010193 ◽

2020 ◽

Vol 12 (1) ◽

pp. 193 ◽

Cited By ~ 1

Author(s):

Chaoxing Sun ◽

Guohe Huang ◽

Yurui Fan

Keyword(s):

Loess Plateau ◽

Return Period ◽

Extreme Precipitation ◽

Precipitation Amount ◽

Heavy Precipitation ◽

The Loess Plateau ◽

Daily Precipitation Data ◽

Joint Return ◽

The unique characteristics of topography, landforms, and climate in the Loess Plateau make it especially important to investigate its extreme precipitation characteristics. Daily precipitation data of Loess Plateau covering a period of 1959–2017 are applied to evaluate the probability features of five precipitation indicators: the amount of extreme heavy precipitation (P95), the days with extreme heavy precipitation, the intensity of extreme heavy precipitation (I95), the continuous dry days, and the annual total precipitation. In addition, the joint risk of different combinations of precipitation indices is quantitatively evaluated based on the copula method. Moreover, the risk and severity of each extreme heavy precipitation factor corresponding to 50-year joint return period are achieved through inverse derivation process. Results show that the precipitation amount and intensity of the Loess Plateau vary greatly in spatial distribution. The annual precipitation in the northwest region may be too concentrated in several rainstorms, which makes the region in a serious drought state for most of the year. At the level of 10-year return period, more than five months with no precipitation events would occur in the Northwest Loess Plateau. While, P95 or I95 events of 100-year level may be encountered in a 50-year return period and in the southeastern region, which means there are foreseeable long-term extreme heavy precipitation events.

Spatial Distribution and Temporal Change of Extreme Precipitation Events on the Koshi Basin of Nepal

Journal on Geoinformatics, Nepal ◽

10.3126/njg.v17i1.23007 ◽

2018 ◽

Vol 17 (1) ◽

pp. 38-46

Author(s):

Sanjeevan Shrestha ◽

Tina Baidar

Keyword(s):

Adverse Effect ◽

Spatial Distribution ◽

Extreme Events ◽

Extreme Precipitation ◽

Temporal Change ◽

Extreme Weather Events ◽

Increasing Trend ◽

Koshi Basin ◽

Climate change, particularly at South Asia region is having a huge impact on precipitation patterns, its intensity and extremeness. Mountainous area is much sensitive to these extreme events, hence having adverse effect on environment as well as people in term of fluctuation in water supply as well as frequent extreme weather events such as flood, landslide etc. So, prediction of extreme precipitation is imperative for proper management. The objective of this study was to assess the spatial distribution and temporal change of extreme precipitation events on Koshi basin of Nepal during 1980-2010. Five indicators (R1day, R5 day, R > 25.4 mm, SDII and CDD) were chosen for 41 meteorological stations to test the extreme events. Inverse distance weighting and kriging interpolation technique was used to interpolate the spatial patterns. Result showed that most extreme precipitation events increased up to mountain regions from low river valley; and then it decreased subsequently up to Himalayan regions (south to north direction). However, there is high value of indices for lowland Terai valley also. Most of the indices have hotspot with higher value at north western and southern part of the study area. For temporal change, most of the extreme precipitation indices showed increasing trend within 30 years’ period. The spatial distribution of temporal change in indices suggests that there is increasing trend in lowland area and decreasing trend in mountainous and Himalayan area. So, adaptive measure should be adopted through proper land use planning, especially at those hotspot areas and their tributaries; to reduce adverse effect of extreme precipitation events.

Scale-dependent representation of extreme precipitation processes in regional and CPM scale simulations for the greater Alpine region

10.5194/egusphere-egu21-4852 ◽

2021 ◽

Author(s):

Alberto Caldas-Alvarez ◽

Hendrik Feldmann ◽

Joaquim G. Pinto

Keyword(s):

Extreme Precipitation ◽

Rare Events ◽

Potential Temperature ◽

Low Pressure ◽

Scale Dependency ◽

Data Set ◽

Spatial Coverage ◽

Precipitation Processes ◽

Extreme precipitation events with return periods above 100-years (Most Extreme Precipitation Events; MEPE) are rare events by definition, as the observational record covers very few of such events. Therefore, our knowledge is insufficient to assess their potential intensities and physical processes on different scales. To fill this gap, large regional climate ensembles, like the one provided by the German Decadal Climate Predictions (MiKlip) project (> 10.000 years), are of great value as they provide a larger sample size of such rare events. The RCM ensemble samples present day climate conditions multiple times (Ehmele et al., 2020) with a resolution of 25 km, and thus it does not resolve the convection permitting scales (CPM).In this study, we aim to combine the large RCM ensemble with episodic CPM-scale downscaling simulations to derive a better statistical and process related representation of MEPEs for Central Europe. As a first step, we evaluate two re-analysis driven long-term simulations with COSMO-CLM (CCLM) from MiKlip and CORDEX-FPS Convection with respect to their scale-dependent representation.The simulations span the period 1971 to 2016 with the 25 km simulation and are forced by ERA40 until 1979 and by ERA-interim afterwards. The CPM simulation (~3 km) is forced by ERA-40 between 1971 and 1999 and by ERA-interim between 2000 and 2016. We validate the simulations against E-OBS (25 km) and the unique HYdrologische RASterdatens&#228;tze (HYRAS) precipitation data set (5 km). The investigation area is the greater Alpine area. We employ a Precipitation Severity Index (PSI) adapted from extreme wind detection (Leckebusch et al., 2008; Pinto et al., 2012) for extreme precipitation cases. The advantage of the PSI is its ability to account for extreme grid point precipitation as well as spatial coverage and event duration. The events are categorized objectively into composite Weather Types (WT) to enable further generalization of the findings.The results show a clear overestimation of precipitation for the analysed period and area by the RCMs at both resolutions. However, large differences exist the representation of extreme precipitation. Compared to observations, the 3 km (25km) resolution overestimates (underestimates) precipitation intensity for extreme cases. This agrees with previous literature. Five different WTs are identified for the analysed period, with Autumn-Winter WT being the most common, followed by convective summer WT. The Autumn-Winter WT is characterized by deep, cold, low-pressure areas located over Northern Europe. Summer WT cases are characterized by stable high-pressure situations affected by incurring small low-pressure systems on its western flank (convective-prone situations). Regarding the scale dependency of precipitation processes, the coarse resolution tends to overestimate surface moisture in situations of heavy precipitation, leading to larger latent instability (CAPE) in the 25 km resolution than in its 3 km counterpart. Furthermore, a large-scale dependency is found in summer extreme precipitation cases for two stability-related variables, Equivalent Potential Temperature (&#952;e850) at 850 hPa and moisture flux at the Lower Free Troposphere (LFT-moisture). In these cases, the overestimation (underestimation) of &#160;and LFT-moisture by either resolution is in line with their precipitation overestimation (underestimation).

Tuning Extreme NEXRAD and CMORPH Precipitation Estimates

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0146.1 ◽

2014 ◽

Vol 15 (3) ◽

pp. 1070-1077 ◽

Cited By ~ 2

Author(s):

Jonathan Woody ◽

Robert Lund ◽

Mekonnen Gebremichael

Keyword(s):

Pareto Distribution ◽

Generalized Pareto Distribution ◽

Heavy Precipitation ◽

Generalized Pareto ◽

Precipitation Estimates ◽

Alternative Sources ◽

Climate Prediction Center ◽

Adequate Ground ◽

Abstract High-resolution satellite precipitation estimates, such as the Climate Prediction Center morphing technique (CMORPH), provide alternative sources of precipitation data for hydrological applications, especially in regions where adequate ground-based instruments are unavailable. These estimates are, however, subject to large errors, especially at times of heavy precipitation. This paper presents a method to distributionally convert a set of CMORPH estimates into ground-based Next Generation Weather Radar (NEXRAD) estimates. As our concern lies with floods and extreme precipitation events, a peaks-over-threshold extreme value approach is adopted that fits a generalized Pareto distribution to the large precipitation estimates. A quantile matching transformation is then used to convert CMORPH values into NEXRAD values. The methods are applied in the analysis of 6 yr of precipitation observations from 625 pixels centered around eastern Oklahoma.

Spatial Distribution of Extreme Precipitation Events and Its Trend in Nepal

Applied Ecology and Environmental Sciences ◽

10.12691/aees-9-1-8 ◽

2020 ◽

Vol 9 (1) ◽

pp. 58-66

Author(s):

Dibas Shrestha ◽

Shankar Sharma ◽

Kalpana Hamal ◽

Umair Khan Jadoon ◽

Binod Dawadi

Keyword(s):

Spatial Distribution ◽

Extreme Precipitation ◽

Evaluating the extreme precipitation events using a mesoscale atmosphere model and satellite based precipitation product

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-1-6979-2013 ◽

2013 ◽

Vol 1 (6) ◽

pp. 6979-7014

Author(s):

I. Yucel ◽

A. Onen

Keyword(s):

Data Assimilation ◽

Extreme Precipitation ◽

Heavy Rainfall ◽

Wrf Model ◽

Hydrologic Model ◽

Rainfall 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 West Black Sea Region of Turkey. Precipitations derived from WRF model with and without three-dimensional variational (3-DVAR) data assimilation scheme and MPE algorithm at high spatial resolution (4 km) are compared with gauge precipitation. WRF-derived precipitation showed capabilities in capturing the timing of precipitation extremes and in some extent the spatial distribution and magnitude of the heavy rainfall events wheras MPE showed relatively weak skills in these aspects. WRF skills in estimating such precipitation characteristics are enhanced with the application of 3-DVAR scheme. Direct impact of data assimilation on WRF precipitation reached to 12% and at some points there exists quantitative match for heavy rainfall events, which are critical for hydrological forecast.