Radar data assimilation impact over nowcasting a mesoscale convective system in Catalonia using the WRF model

This study uses the Weather Research and Forecasting model (WRF) and the three-dimensional variational data assimilation system (WRF 3DVAR), in cold and warm starts, with the aimof finding out an appropriate nowcasting method that would have improved the forecast of precipitation maxima in the mesoscale convective system that occurred in Catalonia (NE Spain)on March 21, 2012 at 20 UTC. We assimilated radar data using different configurations, qualitatively verifying the increase of rainwater produced by the assimilation of reflectivity. While in cold starts the best result was obtained with a length scale of 0.75, in warm startsit was necessary to use a length scale of 0.25. We got better results in all cases when radar data assimilation was used, and although one of the cold starts achieved the best result and correctly located precipitation maxima, the forecast amount was still lower than the observations

A discussion about the role of the shortwave schemes on real WRF-ARW simulations. Two case studies: cloudless and cloudy sky

A wide range of approaches for radiative transfer computations leads to several parameterizations. Differences in these approximations bring about distinct results for the radiative fluxes,even under the same atmospheric conditions. Since the transfer of solar and terrestrial radiationrepresents the primordial physical process that shapes the atmospheric circulation, these deviations must have an impact on the numerical weather prediction (NWP) model performance. In this paper, an analysis of the role of shortwave schemes on the Weather Research and Forecasting (WRF-ARW) model is presented. The study compares the effect of four parameterizations(Dudhia, New Goddard, CAM and RRTMG) in two cases: i) cloudless and ii) cloudy sky situations for a domain defined over Catalonia (northeast of the Iberian Peninsula). We analyze thedirect and the indirect feedback between the dynamical aspects and the physical parameterizations driven by changes in the radiative transfer equation computation. The cumulative effect ofthese variations are studied through three simulation windows: current day (0-23 h), day-ahead(24-47 h) and two days ahead (48-71 h). These analyses are focused on several NWP model fields. From the most directly related toshortwave schemes such as global horizontal irradiance or the heating rate profile, to apparently secondary outcomes such as wind speed or cloud composition among others. The differences observed between model runs using different solar parameterizations increase with the simulation horizon, being more important in the cloudy scenario than in the cloudless sky

Results of the meteorological model WRF-ARW over Catalonia, using different parameterizations of convection and cloud microphysics

The meteorological model WRF-ARW (Weather Research and Forecasting - Advanced ResearchWRF) is a new generation model that has a worldwide growing community of users. In theframework of a project that studies the feasibility of implementing it operationally at the Mete-orological Service of Catalonia, a verification of the forecasts produced by the model in severalcases of precipitation observed over Catalonia has been carried out. Indeed, given the impor-tance of precipitation forecasts in this area, one of the main objectives was to study the sensitivityof the model in different configurations of its parameterizations of convection and cloud micro-physics. In this paper, we present the results of this verification for two domains, a 36-km gridsize and one of 12 km grid size, unidirectionally nested to the previous one. In the externaldomain, the evaluation was based on the analysis of the main statistical parameters (ME andRMSE) for temperature, relative humidity, geopotential and wind, and it has been determinedthat the combination using the Kain-Fritsch convective scheme with the WSM5 microphysicalscheme has provided the best results. Then, with this configuration set for the external domain,some forecasts at the nested domain have been done, by combining different convection andcloud microphysics schemes, leading to the conclusion that the most accurate configuration isthe one combining the convective parameterization of Kain-Fritsch and the Thompson micro-physics scheme

Observacions amb radar meteorològic en condicions de bloqueig topogràfic

Els radars meteorològics ubicats en zones muntanyoses experimenten el que es coneix com a bloqueig topogràfic. El bloqueig consisteix en el fet que una part de l'energia emesa pel radar és interceptada per les muntanyes que l'envolten. Aquest efecte pot restringir seriosament l'ús dels escombratges del radar amb menor angle d'elevació sobre l'horitzó. Justament són els escombratges amb elevacions inferiors els que proporcionen informació més útil per a l'estimació de la intensitat de precipitació a nivell del terra (on tenen major interès hidrometeorològic). A causa del bloqueig topogràfic, en zones muntanyoses sovint s'apliquen correccions sobre l'energia rebuda pel radar amb la finalitat de minimitzar els efectes de la topografia sobre les observacions, especialment si es vol obtenir estimacions quantitatives de precipitació (o EQP)

A new methodology to characterise the radar bright band using doppler spectral moments from vertically pointing radar observations

The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new technique to detect the radar BB levels (top, peak and bottom) for Doppler radar spectral moments from the vertically pointing radars applied here to a K-band radar, the MRR-Pro (Micro Rain Radar). The methodology includes signal and noise detection and dealiasing schemes to provide realistic vertical Doppler velocities of precipitating hydrometeors, subsequent calculation of Doppler moments and associated parameters and BB detection and characterisation. Retrieved BB properties are compared with the melting level provided by the MRR-Pro manufacturer software and also with the 0 °C levels for both dry-bulb temperature (freezing level) and wet-bulb temperature from co-located radio soundings in 39 days. In addition, a co-located Parsivel disdrometer is used to analyse the equivalent reflectivity of the lowest radar height bins confirming consistent results of the new signal and noise detection scheme. The processing methodology is coded in a Python program called RaProM-Pro which is freely available in the GitHub repository

Precipitation Type Classification of Micro Rain Radar Data Using an Improved Doppler Spectral Processing Methodology

This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Ze), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (Nw) or the mean mass-weighted raindrop diameter (Dm) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR 0.70). The methodology is available as a Python language program called RaProM at the public github repository

Precipitation type classification of micro rain radar data using an improved doppler spectral processing methodology

This research was funded by the Spanish Government through projects CGL2015-65627-C3-1-R, CGL2015-65627-C3-2-R (MINECO/FEDER), CGL2016-81828-REDT and RTI2018-098693-B-C32 (AEI/FEDER).This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Ze), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (Nw) or the mean mass-weighted raindrop diameter (Dm) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR 0.70). The methodology is available as a Python language program called RaProM at the public github repository

Decoupling between precipitation processes and mountain wave induced circulations observed with a vertically pointing K-Band Doppler radar

Recent studies reported that precipitation and mountain waves induced low tropospheric level circulations may be decoupled or masked by greater spatial scale variability despite generally there is a connection between microphysical processes of precipitation and mountain driven air flows. In this paper we analyse two periods of a winter storm in the Eastern Pyrenees mountain range (NE Spain) with different mountain wave induced circulations and low-level turbulence as revealed by Micro Rain Radar (MRR), microwave radiometer and Parsivel disdrometer data during the Cerdanya-2017 field campaign. We find that during the event studied mountain wave wind circulations and low-level turbulence do not affect neither the snow crystal riming or aggregation along the vertical column nor the surface particle size distribution of the snow. This study illustrates that precipitation profiles and mountain induced circulations may be decoupled which can be very relevant for either ground-based or spaceborne remote sensing of precipitation

Multisource data verification of a weather radar surface precipitation type product

Póster presentado en: 10th European Conference on Radar in Meteorology and Hydrology celebrado en Wageningen, Países Bajos, del 1 al 6 de julio de 2018.This study was partly supported by projects CGL2015-65627-C3-2-R (MINECO/FEDER), CGL2016-81828-REDT (MINECO) and DI065/2017 (Industrial Doctorate Programme of the Regional Government of Catalonia