A calibrated and consistent combination of probabilistic forecasts for the exceedance of several precipitation thresholds using neural networks.

<p>In this talk we present a new statistical method for the seamless combination of two different ensemble precipitation forecasts (Nowcasting and NWP) using neural networks (NNs), see [1]. The method generates probabilistic forecasts for the exceedance of a set of predetermined thresholds (from 0.1mm up to 5mm). The aim of the combination model is to produce seamless and calibrated forecasts which outperform both input forecasts for all lead times and which are consistent regarding the considered thresholds. First, the hyper-parameters of the NNs are chosen according to a certain hyper-parameter optimization algorithm (not to be confused with the training of the NNs itself) on a 3-month dataset (dataset A). Then, the resulting NNs are tested via a rolling origin validation scheme on two 3-month datasets (datasets B & C) with different input forecasts each. Datasets A & B contain forecasts of DWD's RadVOR, a radar-based nowcasting system, and Ensemble-MOS, a post-processing system of NWP ensembles made by COSMO-DE-EPS, with a horizontal resolution of 20km, which is a predecessor of ICON-D2-EPS. Ensemble-MOS forecasts were provided for up to +6h, while RadVOR forecasts were available up to +2h. For dataset C, forecasts with a grid size of 2.2km are used from STEPS-DWD, a new implementation of the Short-term Ensemble Prediction System (STEPS) by  DWD, and ICON-D2-EPS as a NWP ensemble system. Forecasts were made up to +6h. In both validation datasets (B & C), the forecasts show the well-known behavior that the nowcasting systems RadVOR & STEPS are superior for short lead times, while NWP forecasts (Ensemble-MOS & ICON-D2-EPS) outperform these systems for later lead times. Based on the comparison of several validation scores (bias, Brier skill score, reliability and reliability diagram) we can show that the combination is indeed calibrated, consistent and outperforms both input forecasts for all lead times. It should be noted that the combination works on dataset C, although the hyper-parameters were chosen based on dataset A, which contains different forecasts for a different grid size.<br><br>[1] P. Schaumann, R. Hess, M. Rempel, U. Blahak and V. Schmidt, A calibrated and consistent combination of probabilistic forecasts for the exceedance of several precipitation thresholds using neural networks. Weather and Forecasting (in print)</p>

Weather constraints on global drone flyability

Small aerial drones are used in a growing number of commercial applications. However, drones cannot fly in all weather, which impacts their reliability for time-sensitive operations. The magnitude and global variability of weather impact is poorly understood. We explore weather-limited drone flyability (the proportion of time drones can fly safely) by comparing historical wind speed, temperature, and precipitation data to manufacturer-reported thresholds of common commercial and weather-resistant drones with a computer simulation. We show that global flyability is highest in warm and dry continental regions and lowest over oceans and at high latitudes. Median global flyability for common drones is low: 5.7 h/day or 2.0 h/day if restricted to daylight hours. Weather-resistant drones have higher flyability (20.4 and 12.3 h/day, respectively). While these estimates do not consider all weather conditions, results suggest that improvements to weather resistance can increase flyability. An inverse analysis for major population centres shows the largest flyability gains for common drones can be achieved by increasing maximum wind speed and precipitation thresholds from 10 to 15 m/s and 0–1 mm/h, respectively.

A calibrated and consistent combination of probabilistic forecasts for the exceedance of several precipitation thresholds using neural networks

The seamless combination of nowcasting and numerical weather prediction (NWP) aims to provide a functional basis for very-short-term forecasts, that are essential e.g. for weather warnings. In this paper we propose a statistical method for precipitation using neural networks (NN) that combines nowcasting data from DWD’s radar based RadVOR system with post-processed forecasts of the high resolving NWP ensemble COSMO-DE-EPS. The postprocessing is performed by Ensemble-MOS of DWD. Whereas the quality of the nowcasting projections of RadVOR is excellent at the beginning, it declines rapidly after about 2 hours. The post-processed forecasts of COSMO-DE-EPS in contrast start with lower accuracy but provide meaningful information on longer forecast ranges. The combination of the two systems is performed for probabilities that the expected precipitation amounts exceed a series of predefined thresholds. The resulting probabilistic forecasts are calibrated and outperform both input systems in terms of accuracy for forecast ranges from 1 to 6 hours as shown by verification.The proposed NN-model generalises a previous statistical model based on extended logistic regression, which was restricted to only one threshold of 0.1 mm. The various layers of the NN-model are related to the conventional design elements (e.g. triangular functions and interaction terms) of the previous model for easier insight.

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

<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>

USAPI USDM: Operational Drought Monitoring in the U.S.-Affiliated Pacific Islands

The U.S. Drought Monitor (USDM) has been the de facto operational drought monitoring product for the United States for the last two decades. For most of this time, its coverage included the 50 States and Puerto Rico. In 2019, coverage was expanded to include the U.S.-Affiliated Pacific Islands (USAPI). The geography, geomorphology, and climatology of the USAPI significantly differ from those of the mainland U.S. (CONUS) and they posed a unique challenge for the USDM authors. Following National Oceanic and Atmospheric Administration (NOAA) priorities for development of products in collaboration with users in what is termed “use-inspired science”, NOAA agencies conducted several workshops to identify data and impacts relevant for, and develop drought monitoring criteria appropriate for, the USAPI. Once the criteria were identified and data processing systems were set up, the USAPI were included as part of the operational USDM drought monitoring beginning in March 2019. The drought monitoring criteria consist of weekly and monthly minimum precipitation thresholds for triggering drought, and they follow the USDM “convergence of evidence” methodology for determining the severity level (Dx) of the drought spell.

Mode Flow Map: An Innovative Enterprise Gis For Better Groundwater Management And Monitoring.

Due to the UAE’s location in a dry belt region, rainfall is discontinuous, precipitation thresholds are highly variable and floodwaters leak into the ground. Thus, groundwater is a precious resource for water supplies, environmental protection, and sustainable development. Moreover, the groundwater salinity is relatively high which leads to reduced productivity of agricultural land and degrades the natural environment, therefore proper management and control of such resource use, especially in UAE where there is an intensive irrigation activity, is critical for a sustainable water management.

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

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.

Assessment of meteorological droughts over Saudi Arabia using surface rainfall observations during the period 1978–2017

This paper discusses the preliminary results of meteorological drought analysis over Saudi Arabia for the period 1978–2017. In conjunction with meteorological observations, datasets from the Climate Prediction Center (CPC), the Merged Analysis of Precipitation (CMAP), the Climatic Research Unit (CRU), and the Tropical Rainfall Measuring Mission (TRMM) are utilized to understand the impact of the spatial distribution of rainfall on drought events. Applying precipitation thresholds allows rainfall classifications such as deficit, scanty, and surplus. Precipitation thresholds are also used to define meteorological droughts in the country, which are categorized as usual, moderate, and severe. It is found that drought events occur in Saudi Arabia due to shortfalls in the dry season, even though there is above normal rainfall in the wet season. There is no case of a shortfall in both the wet and dry seasons causing drought. Saudi Arabian droughts of all categories occurred mostly in the dry season, with fewer in the wet season. Results show that in Saudi Arabia, the last month of the wet season (April) is less prone to drought while the first and last months of the dry season (June and September respectively) are more prone to drought. Spatial distribution of drought climatology is obtained by calculating the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI). Further application-driven studies of projections are needed based on drought indices and climate model output.