scholarly journals Analysis of an Extreme Weather Event in a Hyper Arid Region Using WRF-Hydro Coupling, Station, and Satellite data

2018 ◽  
Author(s):  
Youssef Wehbe ◽  
Marouane Temimi ◽  
Michael Weston ◽  
Naira Chaouch ◽  
Oliver Branch ◽  
...  
Keyword(s):  
Soil Moisture ◽  
United Arab Emirates ◽  
Hydrological Modeling ◽  
Flash Flood ◽  
Pearson Correlation ◽  
Global Radiation ◽  
Wrf Model ◽  
Extreme Weather ◽  
Extreme Weather Event ◽  
Weather Event

Abstract. This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016 using the Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (Hydro). Six-hourly forecasted forcing records at 0.5o spatial resolution, obtained from the NCEP Global Forecast System (GFS), are used to drive the three nested downscaling domains of both standalone WRF and coupled WRF/WRF-Hydro configurations for the recent flood-triggering storm. Ground and satellite observations over the UAE are employed to validate the model results. Precipitation, soil moisture, and cloud fraction retrievals from GPM (30-minute, 0.1o product), AMSR2 (daily, 0.1o product), and MODIS (daily, 5 km product), respectively, are used to assess the model output. The Pearson correlation coefficient (PCC), relative bias (rBIAS) and root-mean-square error (RMSE) are used as performance measures. Results show reductions of 24 % and 13 % in RMSE and rBIAS measures, respectively, in precipitation forecasts from the coupled WRF/WRF-Hydro model configuration, when compared to standalone WRF. The coupled system also shows improvements in global radiation forecasts, with reductions of 45 % and 12 % for RMSE and rBIAS, respectively. Moreover, WRF-Hydro was able to simulate the spatial distribution of soil moisture reasonably well across the study domain when compared to AMSR2 satellite soil moisture estimates, despite a noticeable dry/wet bias in areas where soil moisture is high/low. The demonstrated improvement, at the local scale, implies that WRF-Hydro coupling may enhance hydrologic forecasts and flash flood guidance systems in the region.

scholarly journals Analysis of an extreme weather event in a hyper-arid region using WRF-Hydro coupling, station, and satellite data

2019 ◽  
Vol 19 (6) ◽  
pp. 1129-1149 ◽  
Author(s):  
Youssef Wehbe ◽  
Marouane Temimi ◽  
Michael Weston ◽  
Naira Chaouch ◽  
Oliver Branch ◽  
...  
Keyword(s):  
Soil Moisture ◽  
United Arab Emirates ◽  
Hydrological Modeling ◽  
Pearson Correlation ◽  
Global Radiation ◽  
Model Performance ◽  
Extreme Weather ◽  
Extreme Weather Event ◽  
Arid Environments ◽  
Weather Event

Abstract. This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016, using the Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (WRF-Hydro). Six-hourly forecasted forcing records at 0.5∘ spatial resolution, obtained from the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS), are used to drive the three nested downscaling domains of both standalone WRF and coupled WRF–WRF-Hydro configurations for the recent flood-triggering storm. Ground and satellite observations over the UAE are employed to validate the model results. The model performance was assessed using precipitation from the Global Precipitation Measurement (GPM) mission (30 min, 0.1∘ product), soil moisture from the Advanced Microwave Scanning Radiometer 2 (AMSR2; daily, 0.1∘ product) and the NOAA Soil Moisture Operational Products System (SMOPS; 6-hourly, 0.25∘ product), and cloud fraction retrievals from the Moderate Resolution Imaging Spectroradiometer Atmosphere product (MODATM; daily, 5 km product). The Pearson correlation coefficient (PCC), relative bias (rBIAS), and root-mean-square error (RMSE) are used as performance measures. Results show reductions of 24 % and 13 % in RMSE and rBIAS measures, respectively, in precipitation forecasts from the coupled WRF–WRF-Hydro model configuration, when compared to standalone WRF. The coupled system also shows improvements in global radiation forecasts, with reductions of 45 % and 12 % for RMSE and rBIAS, respectively. Moreover, WRF-Hydro was able to simulate the spatial distribution of soil moisture reasonably well across the study domain when compared to AMSR2-derived soil moisture estimates, despite a noticeable dry and wet bias in areas where soil moisture is high and low. Temporal and spatial variabilities of simulated soil moisture compare well to estimates from the NOAA SMOPS product, which indicates the model's capability to simulate surface drainage. Finally, the coupled model showed a shallower planetary boundary layer (PBL) compared to the standalone WRF simulation, which is attributed to the effect of soil moisture feedback. The demonstrated improvement, at the local scale, implies that WRF-Hydro coupling may enhance hydrological and meteorological forecasts in hyper-arid environments.

scholarly journals Climate effects on the onset of flowering in the United Kingdom

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Nigel Fox ◽  
Anna Maria Jönsson
Keyword(s):  
Climate Change ◽  
Time Series ◽  
United Kingdom ◽  
Response Rates ◽  
Extreme Weather ◽  
Extreme Weather Event ◽  
Negative Response ◽  
Wild Plant ◽  
Weather Event ◽  
The United Kingdom

Abstract Background A warmer climate has consequences for the timing of phenological events, as temperature is a key factor controlling plant development and flowering. In this study, we analyse the effects of the long-term climate change and an extreme weather event on the first flowering day (FFD) of five spring-flowering wild plant species in the United Kingdom. Citizen science data from the UK Woodland Trust were obtained for five species: Tussilago farfara (coltsfoot), Anemone nemorosa (wood anemone), Hyacinthoides non-scripta (bluebell), Cardamine pratensis (cuckooflower) and Alliaria petiolate (garlic mustard). Results Out of the 351 site-specific time series (≥ 15-years of FFD records), 74.6% showed significant negative response rates, i.e. earlier flowering in warmer years, ranging from − 5.6 to − 7.7 days °C−1. 23.7% of the series had non-significant negative response rates, and 1.7% had non-significant positive response rates. For cuckooflower, the response rate was increasingly more negative with decreasing latitudes. The winter of 2007 reflects an extreme weather event, about 2 °C warmer compared to 2006, where the 2006 winter temperatures were similar to the 1961–1990 baseline average. The FFD of each species was compared between 2006 and 2007. The results showed that the mean FFD of all species significantly advanced between 13 and 18 days during the extreme warmer winter of 2007, confirming that FFD is affected by temperature. Conclusion Given that all species in the study significantly respond to ambient near-surface temperatures, they are suitable as climate-change indicators. However, the responses to a + 2 °C warmer winter were both more and less pronounced than expected from an analysis of ≥ 15-year time series. This may reflect non-linear responses, species-specific thresholds and cumulative temperature effects. It also indicates that knowledge on extreme weather events is needed for detailed projections of potential climate change effects.

scholarly journals GIS-based quantitative landslide hazard prediction modelling in natural hillslope, Agra Khola watershed, central Nepal

1970 ◽  
Vol 10 ◽  
pp. 63-70 ◽  
Author(s):  
Prem Bahadur Thapa ◽  
Tetsuro Esaki
Keyword(s):  
Multivariate Analysis ◽  
Prediction Model ◽  
Extreme Weather ◽  
Landslide Hazard ◽  
Extreme Weather Event ◽  
Hazard Mapping ◽  
Weather Event ◽  
Quantitative Technique ◽  
Central Nepal ◽  
Hazard Levels

The influence of geological and geomorphological variables were spatially integrated to develop landslide hazard prediction model in the Agra Khola watershed of central Nepal where a large number of landslides triggered off due to extreme weather event of July 19-21, 1993. A quantitative technique of multivariate analysis was performed to predict elements or observations of landslides successfully into different hazard levels in the area. The predicted landslide hazard was validated and spatially relevancy of the prediction is established. The GIS-based prediction model possessed objectivity and reproducibility, and also improved the landslide hazard mapping in the natural hillslope. doi: 10.3126/bdg.v10i0.1421   Bulletin of the Department of Geology, Tribhuvan University, Kathmandu, Nepal, Vol. 10, 2007, pp. 63-70  

scholarly journals Warmer and wetter winters: characteristics and implications of an extreme weather event in the High Arctic

2014 ◽  
Vol 9 (11) ◽  
pp. 114021 ◽  
Author(s):  
Brage B Hansen ◽  
Ketil Isaksen ◽  
Rasmus E Benestad ◽  
Jack Kohler ◽  
Åshild Ø Pedersen ◽  
...  
Keyword(s):  
High Arctic ◽  
Extreme Weather ◽  
Extreme Weather Event ◽  
Weather Event

scholarly journals Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters

2020 ◽  
Vol 12 (8) ◽  
pp. 1342
Author(s):  
Youssef Wehbe ◽  
Marouane Temimi ◽  
Robert F. Adler
Keyword(s):  
Soil Moisture ◽  
United Arab Emirates ◽  
Arid Regions ◽  
Thermal Emission ◽  
Pearson Correlation ◽  
Weather Radar ◽  
Ann Model ◽  
Rain Gauges ◽  
Satellite Retrievals ◽  
Precipitation Estimates

Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest product release (IMERG V06B) locally over the UAE. Two distinct approaches, namely, geographically weighted regression (GWR), and artificial neural networks (ANNs) are tested. Daily soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission (9 km), terrain elevations from the Advanced Spaceborne Thermal Emission and Reflection digital elevation model (ASTER DEM, 30 m) and precipitation estimates (0.5 km) from a weather radar network are incorporated as explanatory variables in the proposed GWR and ANN model frameworks. First, the performances of the daily GPM and weather radar estimates are assessed using a network of 65 rain gauges from 1 January 2015 to 31 December 2018. Next, the GWR and ANN models are developed with 52 gauges used for training and 13 gauges reserved for model testing and seasonal inter-comparisons. GPM estimates record higher Pearson correlation coefficients (PCC) at rain gauges with increasing elevation (z) and higher rainfall amounts (PCC = 0.29 z0.12), while weather radar estimates perform better for lower elevations and light rain conditions (PCC = 0.81 z−0.18). Taylor diagrams indicate that both the GWR- and the ANN-adjusted precipitation products outperform the original GPM and radar estimates, with the poorest correction obtained by GWR during the summer period. The incorporation of soil moisture resulted in improved corrections by the ANN model compared to the GWR, with relative increases in Nash–Sutcliffe efficiency (NSE) coefficients of 56% (and 25%) for GPM estimates, and 34% (and 53%) for radar estimates during summer (and winter) periods. The ANN-derived precipitation estimates can be used to force hydrological models over ungauged areas across the UAE. The methodology is expandable to other arid and hyper-arid regions requiring improved precipitation monitoring.

Campylobacteriosis Risk Of Hospital Admission Related To Extreme Weather Event Frequency In Maryland

2015 ◽  
Vol 2015 (1) ◽  
pp. 2267
Author(s):  
Sutyajeet Soneja ◽  
Chengsheng Jiang ◽  
Clifford Mitchell ◽  
Amy Sapkota ◽  
Amir Sapkota
Keyword(s):  
Hospital Admission ◽  
Extreme Weather ◽  
Extreme Weather Event ◽  
Weather Event ◽  
Event Frequency
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