Efficient dynamical downscaling of general circulation models using continuous data assimilation

Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long- and short-scale features in the downscaled fields using the Weather Research and Forecast (WRF) model is further presented and analyzed. The WRF model is configured at 25km horizontal resolution and is driven by 250km initial and boundary conditions from NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a one-month period in January, 2016. The similarity metric is used to evaluate the performance of the downscaling methods for large and small scales. Similarity results are compared for the outputs of the WRF model with different downscaling techniques, NCEP reanalysis, and Final Analysis. Both spectral nudging and CDA describe better the small-scale features compared to grid nudging. The choice of the wave number is critical in spectral nudging; increasing the number of retained frequencies generally produced better small-scale features, but only up to a certain threshold after which its solution gradually became closer to grid nudging. CDA maintains the balance of the large- and small-scale features similar to that of the best simulation achieved by the best spectral nudging configuration, without the need of a spectral decomposition. The different downscaled atmospheric variables, including rainfall distribution, with CDA is most consistent with the observations. The Brier skill score values further indicate that the added value of CDA is distributed over the entire model domain. The overall results clearly suggest that CDA provides an efficient new approach for dynamical downscaling by maintaining better balance between the global model and the downscaled fields

Data Assimilation in high resolution Numerical Weather Prediction models to improve forecast skill of extreme hydrometeorological events.

The complex orography typical of the Mediterranean area supports the formation, mainly during the fall season, of the so-called back-building Mesoscale Convective Systems (MCS) producing torrential rainfall often resulting into flash floods. These events are hardly predictable from a hydrometeorological standpoint and may cause significant amount of fatalities and socio-economic damages. Liguria region is characterized by small catchments with very short hydrological response time, and it has been proven to be very exposed to back-building MCSs occurrence. Indeed this region between 2011 and 2014 has been hit by three intense back-building MCSs causing a total death toll of 20 people and several hundred million of euros of damages. Building on the existing relationship between significant lightning activity and deep convection and precipitation, the first part of this work assesses the performance of the Lightning Potential Index, as a measure of the potential for charge generation and separation that leads to lightning occurrence in clouds, for the back-building Mesoscale Convective System which hit Genoa city (Italy) in 2014. An ensemble of Weather Research and Forecasting simulations at cloud-permitting grid spacing (1 km) with different microphysical parameterizations is performed and compared to the available observational radar and lightning data. The results allow gaining a deeper understanding of the role of lightning phenomena in the predictability of back-building Mesoscale Convective Systems often producing flash flood over western Mediterranean complex topography areas. Despite these positive and promising outcomes for the understanding highly-impacting MCS, the main forecasting issue, namely the uncertainty in the correct reproduction of the convective field (location, timing, and intensity) for this kind of events still remains open. Thus, the second part of the work assesses the predictive capability, for a set of back-building Liguria MCS episodes (including Genoa 2014), of a hydro-meteorological forecasting chain composed by a km-scale cloud resolving WRF model, including a 6 hour cycling 3DVAR assimilation of radar reflectivity and conventional ground sensors data, by the Rainfall Filtered Autoregressive Model (RainFARM) and the fully distributed hydrological model Continuum. A rich portfolio of WRF 3DVAR direct and indirect reflectivity operators, has been explored to drive the meteorological component of the proposed forecasting chain. The results confirm the importance of rapidly refreshing and data intensive 3DVAR for improving first quantitative precipitation forecast, and, subsequently flash-floods occurrence prediction in case of back-building MCSs events. The third part of this work devoted the improvement of severe hydrometeorological events prediction has been undertaken in the framework of the European Space Agency (ESA) STEAM (SaTellite Earth observation for Atmospheric Modelling) project aiming at investigating, new areas of synergy between high-resolution numerical atmosphere models and data from spaceborne remote sensing sensors, with focus on Copernicus Sentinels 1, 2 and 3 satellites and Global Positioning System stations. In this context, the Copernicus Sentinel satellites represent an important source of data, because they provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed, columnar water vapor) to be used in NWP models runs operated at cloud resolving grid spacing . For this project two different use cases are analyzed: the Livorno flash flood of 9 Sept 2017, with a death tool of 9 people, and the Silvi Marina flood of 15 November 2017. Overall the results show an improvement of the forecast accuracy by assimilating the Sentinel-1 derived wind and soil moisture products as well as the Zenith Total Delay assimilation both from GPS stations and SAR Interferometry technique applied to Sentinel-1 data

Storm microphysics and kinematics at the ARM-SGP site using dual polarized radar observations at multiple frequencies

2014 Fall.Includes bibliographical references.This research utilizes observations from the Atmospheric Radiation Measurement (ARM) Climate Research Facility at the Southern Great Plains location to investigate the kinematic and microphysical processes present in various types of weather systems. The majority of the data used was collected during the Mid-latitude Continental Convective Cloud Experiment (MC3E), and utilizes the network of scanning radars to arrive at a multi-Doppler wind retrieval and is compared to vertical wind measurements from a centrally located profiling radar. Microphysical compositions of the storms are analyzed using a multi-wavelength hydrometeor identification algorithm utilizing the strengths of each of the radar wavelengths available (X, C, S). When available, a comparison is done between observational analysis and simulated model output from the Weather Research Forecasting model with Spectral-bin Microphysics (WRF-SBM) using bulk statistics to look at reflectivity, vertical motions, and microphysics

WRF-Model Data Assimilation Studies of Landfalling Atmospheric Rivers and Orographic Precipitation Over Northern California

In this study, data assimilation methods of 3-D variational analysis (3DVAR), observation nudging, and analysis (grid) nudging were evaluated in the Weather Research and Forecasting (WRF) model for a high-impact, multi-episode landfalling atmospheric river (AR) event for Northern California from 28 November to 3 December, 2012. Eight experiments were designed to explore various combinations of the data assimilation methods and different initial conditions. The short-to-medium range quantitative precipitation forecast (QPF) performances were tested for each experiment. Surface observations from the National Oceanic and Atmospheric Administration\u27s (NOAA) Hydrometeorology Network (HMT), National Weather Service (NWS) radiosondes, and GPS Radio Occultation (RO) vertical profiles from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) satellites were used for assimilation. Model results 2.5 days into the forecast showed slower timing of the 2nd AR episode by a few hours and an underestimation in AR strength. For the entire event forecasts, the non-grid-nudging experiments showed the lowest mean absolute error (MAE) for rainfall accumulations, especially those with 3DVAR. Higher-resolution initial conditions showed more realistic coastal QPFs. Also, a 3-h nudging time interval and time window for observation nudging and 3DVAR, respectively, may be too large for this type of event, and it did not show skill until 60-66 h into the forecast

Simulating aerosol–radiation–cloud feedbacks on meteorology and air quality over eastern China under severe haze conditionsin winter

The aerosol-radiation-cloud feedbacks on meteorology and air quality over eastern China under severe winter haze conditions in January 2013 are simulated using the fully coupled online Weather Research and Forecasting/Chemistry (WRF-Chem) model. Three simulation scenarios including different aerosol configurations are undertaken to distinguish the aerosol's radiative (direct and semi-direct) and indirect effects. Simulated spatial and temporal variations of PM2.5 are generally consistent with surface observations, with a mean bias of −18.9 μg m−3 (−15.0%) averaged over 71 big cities in China. Comparisons between different scenarios reveal that aerosol radiative effects (direct effect and semi-direct effects) result in reductions of downward shortwave flux at the surface, 2 m temperature, 10 m wind speed and planetary boundary layer (PBL) height by up to 84.0 W m−2, 3.2°C, 0.8 m s−1, and 268 m, respectively. The simulated impact of the aerosol indirect effects is comparatively smaller. Through reducing the PBL height and stabilizing lower atmosphere, the aerosol effects lead to increases in surface concentrations of primary pollutants (CO and SO2). Surface O3 mixing ratio is reduced by up to 6.9 ppb (parts per billion) due to reduced incoming solar radiation and lower temperature, while the aerosol feedbacks on PM2.5 mass concentrations show some spatial variations. Comparisons of model results with observations show that inclusion of aerosol feedbacks in the model significantly improves model performance in simulating meteorological variables and improves simulations of PM2.5 temporal distributions over the North China Plain, the Yangtze River delta, the Pearl River delta, and central China. Although the aerosol–radiation–cloud feedbacks on aerosol mass concentrations are subject to uncertainties, this work demonstrates the significance of aerosol–radiation–cloud feedbacks for real-time air quality forecasting under haze conditions

Evaluación de esquemas de microfísica WRF en la simulación de una línea de turbonada sobre IRAN utilizando datos de radar y de reanálisis

Se registró una línea de turbonada en el puerto de Dayyer, al suroeste de Irán, el 19 de marzo de 2017. En el presente documento, hemos simulado los rasgos característicos asociados con la línea de turbonada mediante el modelo de investigación y pronóstico meteorológico (WRF) utilizando cinco microfísicas diferentes (MP) esquemas. Para validar las características simuladas de la línea de turbonada, la reflectividad de la sección transversal de latitud-altura y longitud-altura y el valor de precipitación derivado de la reflectividad observada recopilada por el radar meteorológico Doppler en Bushehr, datos de la estación meteorológica sinóptica en el puerto de Dayyer junto con NCEP-NCAR y ERA -Se utilizaron datos de reanálisisINTERIM. Para verificar la precipitación simulada, se calculó la curva Fractions Skill Score (FSS). El examen de los resultados de la simulación de la presión geopotencial y al nivel del mar muestra que las simulaciones del modelo que utilizan diferentes esquemas de MP concuerdan bien con los reanálisis de verificación. Además, la distribución espacial de las precipitaciones de las simulaciones y las observaciones de verificación no mostraron grandes diferencias. Sin embargo, existen diferencias significativas en los detalles de las simulaciones, como la reflectividad máxima de las celdas convectivas, la extensión vertical de las celdas de tormenta, la velocidad y dirección del viento, los valores de precipitación y las curvas FSS. Sin embargo, todas las simulaciones han mostrado celdas convectivas sobre el puerto de Dayyer en el momento de la aparición de la línea de turbonada, pero solo la simulación del modelo que usa el esquema Lin MP es consistente con la reflectividad del radar y la extensión vertical correspondientes. El gráfico FSS mostró que la habilidad cambia con la escala espacial. Los resultados utilizando el esquema de microfísica Lin cruzaron la línea FSSuniform a escalas más bajas en comparación con otros esquemas de M