scholarly journals Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 11
Author(s):  
Riccardo Angelo Giro ◽  
Lorenzo Luini ◽  
Carlo Giuseppe Riva
Keyword(s):  
Numerical Weather Prediction ◽  
Rain Rate ◽  
Weather Prediction ◽  
Inversion Algorithm ◽  
Model Accuracy ◽  
Satellite Links ◽  
Propagation Experiment ◽  
Rain Events ◽  
Downlink Signal ◽  
The One

A novel methodology for estimating rainfall rate from satellite signals is presented. The proposed inversion algorithm yields rain rate estimates by making opportunistic use of the downlink signal and exploiting local ancillary meteorological information (0 °C isotherm height and monthly convectivity index), which can be extracted on a Global basis from Numerical Weather Prediction (NWP) products. The methodology includes different expressions to take the different impact of stratiform and convective rain events on the link into due account. The model accuracy in predicting the rain rate is assessed (and compared to the one of other models), both on a statistical and on an instantaneous basis, by exploiting a full year of data collected in Milan, in the framework of the Alphasat Aldo Paraboni propagation experiment.

Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
C. Cornaro ◽  
F. Bucci ◽  
M. Pierro ◽  
F. Del Frate ◽  
S. Peronaci ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Numerical Weather ◽  
The One ◽  
Model Output Statistics ◽  
Photovoltaic Plant ◽  
University Of Rome ◽  
The University ◽  
Persistence Model

In this paper, several models to forecast the hourly solar irradiance with a day in advance using artificial neural network techniques have been developed and analyzed. The forecast irradiance is the one incident on the plane of the modules array of a photovoltaic plant. Pure statistical (ST) models that use only local measured data and model output statistics (MOS) approaches to refine numerical weather prediction data are tested for the University of Rome “Tor Vergata” site. The performance of ST and MOS, together with the persistence model (PM), is compared. The ST models improve the performance of the PM of around 20%. The combination of ST and NWP in the MOS approach gives the best performance, improving the forecast of approximately 39% with respect to the PM.

scholarly journals Monitoring of rainfall by ground-based passive microwave systems: models, measurements and applications

2005 ◽  
Vol 2 ◽  
pp. 259-265 ◽  
Author(s):  
F. S. Marzano ◽  
D. Cimini ◽  
R. Ware
Keyword(s):  
Great Plains ◽  
Rain Rate ◽  
Simulated Data ◽  
Rain Gauge ◽  
Large Set ◽  
60 Ghz ◽  
Inversion Algorithm ◽  
Precipitation Regimes ◽  
Atmospheric Radiation Measurement ◽  
Rain Events

Abstract. A large set of ground-based multi-frequency microwave radiometric simulations and measurements during different precipitation regimes are analysed. Simulations are performed for a set of frequencies from 22 to 60 GHz, representing the channels currently available on an operational ground-based radiometric system. Results are illustrated in terms of comparisons between measurements and model data in order to show that the observed radiometric signatures can be attributed to rainfall scattering and absorption. An inversion algorithm has been developed, basing on the simulated data, to retrieve rain rate from passive radiometric observations. As a validation of the approach, we have analyzed radiometric measurements during rain events occurred in Boulder, Colorado, and at the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains (SGP) site in Lamont, Oklahoma, USA, comparing rain rate estimates with available simultaneous rain gauge data.

scholarly journals Experience of using the Kalman filter to correct numerical forecasts of surface air temperature

2020 ◽  
Vol 4 ◽  
pp. 28-42
Author(s):  
Yu.V. Alferov . ◽  
◽  
E.G. Klimova ◽  
Keyword(s):  
Kalman Filter ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Air Temperature ◽  
One Dimensional ◽  
Numerical Weather ◽  
Statistical Correction ◽  
The One

A possibility of using the one-dimensional Kalman filter to improve the forecast of surface air temperature at an irregular grid of point is studied. This mechanism is tested using the forecasts obtained from different configurations of two different numerical weather prediction models. An algorithm for the statistical correction of numerical forecasts of surface air temperature based on the one-dimensional Kalman filter is constructed. Two methods are proposed for estimating the bias noise dispersion. The series of experiments demonstrated the effectiveness of the algorithm for the bias compensation.The most significantresults are achieved for the models with large bias or for long-range forecasts. At the same time, the use of the algorithm has little effect on the root-meansquare error of the forecast. Keywords: hydrodynamic model of the atmosphere, numerical weather prediction, statistical correction of numerical forecasts, Kalman filter

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

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.

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