scholarly journals Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

2018 ◽  
Vol 10 (10) ◽  
pp. 1549 ◽  
Author(s):  
Elisabetta Ricciardelli ◽  
Francesco Di Paola ◽  
Sabrina Gentile ◽  
Angela Cersosimo ◽  
Domenico Cimini ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Rain Rate ◽  
Weather Prediction ◽  
Weather Radar ◽  
Heavy Rainfall Event ◽  
Qualitative Comparison ◽  
Extreme Rainfall Events ◽  
Numerical Weather ◽  
Radar Network ◽  
Accumulated Rainfall

This study investigates the value of satellite-based observational algorithms in supporting numerical weather prediction (NWP) for improving the alert and monitoring of extreme rainfall events. To this aim, the analysis of the very intense precipitation that affected the city of Livorno on 9 and 10 September 2017 is performed by applying three remote sensing techniques based on satellite observations at infrared/visible and microwave frequencies and by using maps of accumulated rainfall from the weather research and forecasting (WRF) model. The satellite-based observational algorithms are the precipitation evolving technique (PET), the rain class evaluation from infrared and visible observations (RainCEIV) technique and the cloud classification mask coupling of statistical and physics methods (C-MACSP). Moreover, the rain rates estimated by the Italian Weather Radar Network are also considered to get a quantitative evaluation of RainCEIV and PET performance. The statistical assessment shows good skills for both the algorithms (for PET: bias = 1.03, POD = 0.76, FAR = 0.26; for RainCEIV: bias = 1.33, POD = 0.77, FAR = 0.41). In addition, a qualitative comparison among the three technique outputs, rain rate radar maps, and WRF accumulated rainfall maps is also carried out in order to highlight the advantages of the different techniques in providing real-time monitoring, as well as quantitative characterization of rainy areas, especially when rain rate measurements from Weather Radar Network and/or from rain gauges are not available.

scholarly journals The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally

2020 ◽  
Vol 101 (3) ◽  
pp. E341-E356 ◽  
Author(s):  
Juliane Mai ◽  
Kurt C. Kornelsen ◽  
Bryan A. Tolson ◽  
Vincent Fortin ◽  
Nicolas Gasset ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Energy Demand ◽  
Processing Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Demand Forecasting ◽  
Environmental Modeling ◽  
Extreme Rainfall Events ◽  
Numerical Weather ◽  
Surface Prediction

Abstract The Canadian Surface Prediction Archive (CaSPAr) is an archive of numerical weather predictions issued by Environment and Climate Change Canada. Among the products archived on a daily basis are five operational numerical weather forecasts, three operational analyses, and one reanalysis product. The products have hourly to daily temporal resolution and 2.5–50-km spatial resolution. To date the archive contains 394 TB of data while 368 GB of new data are added every night. The data are archived in CF-1.6-compliant netCDF-4 format. The archive is available online (https://caspar-data.ca) since June 2017 and allows users to precisely request data according to their needs, that is, spatial cropping based on a standard shape or uploaded shapefile of the domain of interest and selection of forecast horizons, variables, and issue dates. The degree of customization in CaSPAr is a unique feature relative to other publicly accessible numerical weather prediction archives and it minimizes user download requirements and local processing time. We benchmark the processing time and required storage of such requests based on 216 test scenarios. We also demonstrate how CaSPAr data can be employed to analyze extreme rainfall events. CaSPAr provides access to data that are fundamental for evaluating numerical weather prediction models and demonstrating the improvement in products such as flood and energy demand forecasting systems.

Small-Scale Horizontal Rain-Rate Variability Observed by Satellite

2006 ◽  
Vol 134 (10) ◽  
pp. 2722-2733 ◽  
Author(s):  
Atul K. Varma ◽  
Guosheng Liu
Keyword(s):  
Numerical Weather Prediction ◽  
Rain Rate ◽  
Prediction Models ◽  
Weather Prediction ◽  
Conditional Probability Density ◽  
Numerical Weather ◽  
Stratiform Rain ◽  
Satellite Microwave ◽  
Rain Rates ◽  
Numerical Weather Prediction Models

Abstract The horizontal distribution of rain rates within an area comparable to the pixel size of satellite microwave radiometers and the grid size of numerical weather prediction models has been studied over the global Tropics using three years of the Tropical Rainfall Measuring Mission satellite precipitation radar (PR) data. The global distribution of rain-rate standard deviation derived from the PR data suggests that the horizontal variability of rain rates is largely influenced by two factors: surface type (land or ocean) and latitudinal location (tropical or extratropical). Except for light stratiform rain, the land–ocean contrast seems to be the dominant feature for the differences in conditional probability density functions (PDFs) of rain rate. That is, oceanic rain-rate distribution is narrower when the rain rate is low, but becomes broader when the rain rate is high. For light stratiform rain, there is no clear difference among the rain-rate PDFs for rain events over land and ocean. The latitudinal variation of rain-rate PDFs seems to be greater for heavy rain than for light rain. In particular, there is no measurable difference in overland convective rain-rate PDFs between the Tropics and extratropics. Based on three years of observational data, two attributes, fractional rain cover and conditional rain-rate PDFs, are parameterized as a function of 0.25° × 0.25° areal rain rate. These parameterizations are particularly useful in satellite microwave rainfall retrieval and assimilation of satellite microwave radiance data in numerical weather prediction models.

scholarly journals Improving Numerical Weather Prediction of Rainfall Events Using Radar Data Assimilation

2019 ◽  
Vol 51 (3) ◽  
pp. 273 ◽  
Author(s):  
Miranti Indri Hastuti ◽  
Jaka Anugrah Ivanda Paski ◽  
Fatkhuroyan Fatkhuroyan
Keyword(s):  
Numerical Model ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Weather Radar ◽  
Radar Data ◽  
Observation Data ◽  
Numerical Weather ◽  
Rain Events ◽  
Radar Data Assimilation

Data assimilation is one of method to improve initial atmospheric conditions data in numerical weather prediction. The assimilation of weather radar data that has quite extensive and tight data is considered to be able to improve the quality of weather prediction and analysis. This study aims to investigate the effect of assimilation of Doppler weather radar data in Weather Research Forecasting (WRF) numerical model for the prediction of heavy rain events in the Jabodetabek area with dates representing four seasons respectively on 20 February 2017, 3 April 2017, 13 June 2017, and 9 November 2017. For this purpose, the reflectivity (Z) and radial velocity (V) data from Plan Position Indicator (PPI) product and reflectivity (Z) data from Constant Altitude PPI (CAPPI) product were assimilated using WRFDA (WRF Data Assimilation) numerical model with 3DVar (The Three Dimensional Variational) system. The output of radar data assimilation and without assimilation of the numerical model of WRF is verified by spatial with GSMaP data and by point with precipitation observation data. In general, WRF radar assimilation provides a better simulation of spatial and point rain events compared to the WRF model without assimilation which is improvements of rain prediction from WRF radar data assimilation would be more visible in areas close to radar sources and not echo-blocked from fixed objects, and more visible during the rainy season

Predictability of Precipitation Caused by Linear Precipitation Systems During the July 2017 Northern Kyushu Heavy Rainfall Event Using a Cloud-Resolving Numerical Weather Prediction Model

2018 ◽  
Vol 13 (5) ◽  
pp. 846-859 ◽  
Author(s):  
Ryohei Kato ◽  
◽  
Ken-ichi Shimose ◽  
Shingo Shimizu
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Prediction Accuracy ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Quantitative Prediction ◽  
Initial Time ◽  
Numerical Weather ◽  
Accumulated Rainfall ◽  
Torrential Rainfall

Torrential rainfall associated with linear precipitation systems occurred in Northern Kyushu, Japan, during July 5–6, 2017, causing severe damage in Fukuoka and Oita Prefectures. According to our statistical survey using ground rain gauges, the torrential rainfall was among the heaviest in recorded history for 6- and 12-h accumulated rainfall, and was unusual because heavy rain continued locally for nine hours. The predictability of precipitation associated with linear precipitation systems for this event was investigated using a cloud-resolving numerical weather prediction model with a horizontal grid interval of 1 km. The development of multiple linear precipitation systems was predicted in experiments whose initial calculation time was from several hours to immediately before the torrential rain (9:00, 10:00, 11:00, and 12:00 Japan Standard Time on July 5), although there were some displacement errors in the predicted linear precipitation systems. However, the stationary linear precipitation systems were not properly predicted. The predictions showed that the linear precipitation systems formed one after another and moved eastwards. In the relatively accurate prediction whose initial time was 12:00 on July 5, immediately before the torrential rainfall began, the forecast accuracy was evaluated using the 6-h accumulated precipitation (P6h) from 12:00 to 18:00 on July 5, the period of the heaviest rainfall. The average of the P6h in an area 100 km×40 km around the torrential rainfall area was nearly the same for the analysis and the prediction, indicating that the total precipitation amount around the torrential rainfall area was predictable. The result of evaluating the quantitative prediction accuracy using the Fractions Skill Score (FSS) indicated that a difference in location of 25 km (50 km) or greater should be allowed for in the models to produce useful predictions (those defined as having an FSS ≥0.5) for the accumulated rainfall of P6h ≥50 mm (150 mm). The quantitative prediction accuracy examined in this study can be basic information to investigate the usage of predicted precipitation data.

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