scholarly journals Impact of airborne cloud radar reflectivity data assimilation on kilometre-scale NWP analyses and forecasts of heavy precipitation events

2019 ◽  
Author(s):  
Anonymous
Keyword(s):  
Data Assimilation ◽  
Heavy Precipitation ◽  
Radar Reflectivity ◽  
Cloud Radar ◽  
Reflectivity Data ◽  
Precipitation Events

scholarly journals Impact of airborne cloud radar reflectivity data assimilation on kilometre-scale NWP analyses and forecasts of heavy precipitation events

2018 ◽  
Author(s):  
Mary Borderies ◽  
Olivier Caumont ◽  
Julien Delanoë ◽  
Véronique Ducrocq ◽  
Nadia Fourrié ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Heavy Precipitation ◽  
Mediterranean Area ◽  
Radar Reflectivity ◽  
Wind Data ◽  
Cloud Radar ◽  
W Band ◽  
Assimilation System ◽  
Precipitation Events

Abstract. This article investigates the potential of W-band radar reflectivity to improve the quality of analyses and forecasts of heavy precipitation events in the Mediterranean area. The 1D + 3DVar assimilation method, operationally employed to assimilate ground-based precipitation radar data in the Météo-France kilometre-scale NWP model AROME, has been adapted to assimilate the W-band reflectivity measured by the airborne cloud radar RASTA during a two-month period over the Mediterranean area. After applying a bias correction, vertical profiles of relative humidity are first derived via a 1D Bayesian retrieval, and then used as relative humidity pseudo-observations in the 3DVar assimilation system of AROME. The efficiency of the 1D Bayesian method in retrieving humidity fields is assessed using independent in-flight humidity measurements. To complement this study, the benefit brought by consistent thermodynamic and dynamic cloud conditions has been investigated by assimilating separately and jointly in the 3 h 3DVar assimilation system of AROME the W-band reflectivity and horizontal wind measurements collected by RASTA. The data assimilation experiments are conducted for a single heavy precipitation event, and then for 32 cases. Results indicate that the W-band reflectivity has a larger impact on the humidity, temperature and pressure fields in the analyses, compared to the assimilation of RASTA wind data alone. Besides, the analyses get closer to independent humidity observations if the W-band reflectivity is assimilated alone or jointly with RASTA wind data. Nonetheless, the impact of the W-band reflectivity decreases more rapidly as the forecast range increases, compared to the assimilation of RASTA wind data alone. Generally, the assimilation of the W-band reflectivity jointly with wind data results in the best improvement of the rainfall precipitation forecasts. Consequently, results of this study indicate that consistent thermodynamic and dynamic cloud conditions in the analysis leads to an improvement of both model initial conditions and forecasts. Even though to a less extent, the assimilation of the W-band reflectivity alone also results in a slight improvement of the rainfall precipitation forecasts.

scholarly journals Short-Range Numerical Weather Prediction of Extreme Precipitation Events Using Enhanced Surface Data Assimilation

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 587
Author(s):  
Magnus Lindskog ◽  
Tomas Landelius
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Numerical Weather ◽  
Extreme Precipitation Events ◽  
Surface Data ◽  
Enhanced Surface ◽  
Precipitation Events

A limited-area kilometre scale numerical weather prediction system is applied to evaluate the effect of refined surface data assimilation on short-range heavy precipitation forecasts. The refinements include a spatially dependent background error representation, use of a flow-dependent data assimilation technique, and use of data from a satellite-based scatterometer instrument. The effect of the enhancements on short-term prediction of intense precipitation events is confirmed through a number of case studies. Verification scores and subjective evaluation of one particular case points at a clear impact of the enhanced surface data assimilation on short-range heavy precipitation forecasts and suggest that it also tends to slightly improve them. Although this is not strictly statistically demonstrated, it is consistent with the expectation that a better surface state should improve rainfall forecasts.

scholarly journals Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event during the HyMeX campaign

2017 ◽  
Vol 21 (11) ◽  
pp. 5459-5476 ◽  
Author(s):  
Ida Maiello ◽  
Sabrina Gentile ◽  
Rossella Ferretti ◽  
Luca Baldini ◽  
Nicoletta Roberto ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Initial Conditions ◽  
Flash Flood ◽  
Central Italy ◽  
Heavy Precipitation ◽  
Radar Reflectivity ◽  
Precipitation Event ◽  
Italian Regions ◽  
Reflectivity Data ◽  
The Impact

Abstract. An analysis to evaluate the impact of multiple radar reflectivity data with a three-dimensional variational (3-D-Var) assimilation system on a heavy precipitation event is presented. The main goal is to build a regionally tuned numerical prediction model and a decision-support system for environmental civil protection services and demonstrate it in the central Italian regions, distinguishing which type of observations, conventional and not (or a combination of them), is more effective in improving the accuracy of the forecasted rainfall. In that respect, during the first special observation period (SOP1) of HyMeX (Hydrological cycle in the Mediterranean Experiment) campaign several intensive observing periods (IOPs) were launched and nine of which occurred in Italy. Among them, IOP4 is chosen for this study because of its low predictability regarding the exact location and amount of precipitation. This event hit central Italy on 14 September 2012 producing heavy precipitation and causing several cases of damage to buildings, infrastructure, and roads. Reflectivity data taken from three C-band Doppler radars running operationally during the event are assimilated using the 3-D-Var technique to improve high-resolution initial conditions. In order to evaluate the impact of the assimilation procedure at different horizontal resolutions and to assess the impact of assimilating reflectivity data from multiple radars, several experiments using the Weather Research and Forecasting (WRF) model are performed. Finally, traditional verification scores such as accuracy, equitable threat score, false alarm ratio, and frequency bias – interpreted by analysing their uncertainty through bootstrap confidence intervals (CIs) – are used to objectively compare the experiments, using rain gauge data as a benchmark.

An Approach of Radar Reflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) at Landfall

2007 ◽  
Vol 46 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Qingnong Xiao ◽  
Ying-Hwa Kuo ◽  
Juanzhen Sun ◽  
Wen-Chau Lee ◽  
Dale M. Barker ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Mesoscale Model ◽  
Control Variable ◽  
Three Dimensional ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Mixing Ratio ◽  
Warm Rain Process ◽  
Reflectivity Data

Abstract A radar reflectivity data assimilation scheme was developed within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) system. The model total water mixing ratio was used as a control variable. A warm-rain process, its linear, and its adjoint were incorporated into the system to partition the moisture and hydrometeor increments. The observation operator for radar reflectivity was developed and incorporated into the 3DVAR. With a single reflectivity observation, the multivariate structures of the analysis increments that included cloud water and rainwater mixing ratio increments were examined. Using the onshore Doppler radar data from Jindo, South Korea, the capability of the radar reflectivity assimilation for the landfalling Typhoon Rusa (2002) was assessed. Verifications of inland quantitative precipitation forecasting (QPF) of Typhoon Rusa (2002) showed positive impacts of assimilating radar reflectivity data on the short-range QPF.

scholarly journals AVALIAÇÃO DO WRF-4DVAR EM UM CASO DE EVENTO SEVERO NO RIO GRANDE DO SUL

2016 ◽  
Vol 38 (2) ◽  
pp. 1077
Author(s):  
Luana Ribeiro Macedo ◽  
João Luiz Martins Basso ◽  
Yoshihiro Yamasaki
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Rio Grande ◽  
Real Data ◽  
Radar Reflectivity ◽  
Rio Grande Do Sul ◽  
Mesoscale System ◽  
Accumulated Rainfall ◽  
Reflectivity Data ◽  
The Impact

The WRF mesoscale system 4DVAR data assimilation technique have been used with the purpose of evaluating the impact of the meteorological data assimilation on the numeric time prognosis over the Rio Grande do Sul state. It has been done utilizing the surface and altitude data. The consistency analysis has been done evaluating the numerical prognosis exploring the differences between the analysis with and without data assimilation. The produced prognosis results have been compared spatially using the TRMM satellite data as well as the Canguçu radar reflectivity data. The accumulated rainfall has been validated and compared spatially with the TRMM data for the time period of 12 hours comprehended between October 29th and 30th of 2014. It was possible to realize that as well as the WRF, the WRFVAR overestimated the rainfall values. The radar reflectivity field without data assimilation for October 30th at 06:00UTC detected most accurately the reflectivity centers over the state. On the other hand this field with data assimilation did not present good skill. The temperature field analyses reveal that the 4DVAR assimilation system contributes, one way or another, presenting a little improvement for some points compared to the real data.

scholarly journals Retrievals of ice microphysics using dual-wavelength polarimetric radar observations during stratiform precipitation events

2021 ◽  
Author(s):  
Eleni Tetoni ◽  
Florian Ewald ◽  
Martin Hagen ◽  
Gregor Köcher ◽  
Tobias Zinner ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Shape ◽  
Radar Reflectivity ◽  
Dual Wavelength ◽  
Ice Cloud ◽  
Cloud Radar ◽  
Stratiform Precipitation ◽  
Mass Size ◽  
Ice Microphysics ◽  
Precipitation Events

Abstract. Ice growth processes within clouds affect the type as well as the amount of precipitation. Hence, the importance of an accurate representation of ice microphysics in numerical weather and numerical climate models has been confirmed by several studies. To better constrain ice processes in models, we need to study ice cloud regions before and during monitored precipitation events. For this purpose, two radar instruments facing each other were used to collect complementary measurements. The C-band POLDIRAD weather radar from the German Aerospace Center (DLR), Oberpfaffenhofen and the Ka-band MIRA-35 cloud radar from the Ludwig Maximilians University of Munich (LMU) were used to monitor stratiform precipitation in the vertical cross-section area between both instruments. The logarithmic difference of radar reflectivities at two different wavelengths (54.5 and 8.5 mm), known as dual-wavelength ratio, was exploited to provide information about the size of the detected ice hydrometeors, taking advantage of the different scattering behavior in the Rayleigh and Mie regime. Along with the dual-wavelength ratio, differential radar reflectivity measurements from POLDIRAD provided information about the apparent shape of the detected ice hydrometeors. Scattering simulations using the T-matrix method were performed for oblate and horizontally aligned prolate ice spheroids of varying shape and size using a realistic particle size distribution and a well-established mass-size relationship. The combination of dual-wavelength ratio, radar reflectivity and differential radar reflectivity measurements as well as scattering simulations was used for the development of a novel retrieval for ice cloud microphysics. The development of the retrieval scheme also comprised a method to estimate the hydrometeor attenuation in both radar bands. To demonstrate this approach, a feasibility study was conducted on three stratiform snow events which were monitored over Munich in January 2019. The ice retrieval can obtain ice particle shape, size and mass which are in line with differential radar reflectivity, dual-wavelength ratio and radar reflectivity observations when a suitable mass-size relation is used and when ice hydrometeors are assumed to be represented by oblate ice spheroids. A furthermore finding was the importance of the differential radar reflectivity for the particle size retrieval directly above the MIRA-35 cloud radar. Especially for that observation geometry, the simultaneous slantwise observation from the polarimetric weather radar POLDIRAD could reduce ambiguities in retrieval of the ice particle size by constraining the ice particle shape.

scholarly journals Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model

2014 ◽  
Vol 142 (5) ◽  
pp. 1852-1873 ◽  
Author(s):  
Eric Wattrelot ◽  
Olivier Caumont ◽  
Jean-François Mahfouf
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Short Term ◽  
Background Error ◽  
Reflectivity Data

AbstractThis paper presents results from radar reflectivity data assimilation experiments with the nonhydrostatic limited-area model Application of Research to Operations at Mesoscale (AROME) in an operational context. A one-dimensional (1D) Bayesian retrieval of relative humidity profiles followed by a three-dimensional variational data assimilation (3D-Var) technique is adopted. Several preprocessing procedures of raw reflectivity data are presented and the use of the nonrainy signal in the assimilation is widely discussed and illustrated. This two-step methodology allows the authors to build up a screening procedure that takes into account the evaluation of the results from the 1D Bayesian retrieval. In particular, the 1D retrieval is checked by comparing a pseudoanalyzed reflectivity to the observed reflectivity. Additionally, a physical consistency between the reflectivity innovations and the 1D relative humidity increments is imposed before assimilating relative humidity pseudo-observations with other observations. This allows the authors to counteract the difficulty of the current 3D-Var system to correct strong differences between model and observed clouds from the crude specification of background-error covariances. Assimilation experiments of radar reflectivity data in a preoperational configuration are first performed over a 1-month period. Positive impacts on short-term precipitation forecast scores are systematically found. The evaluation shows improvements on the analysis and also on objective conventional forecast scores, in particular for the model wind field up to 12 h. A case study for a specific precipitating system demonstrates the capacity of the method for improving significantly short-term forecasts of organized convection.

scholarly journals Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

2018 ◽  
Vol 10 (9) ◽  
pp. 1453 ◽  
Author(s):  
Serguei Ivanov ◽  
Silas Michaelides ◽  
Igor Ruban
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Original Data ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Radar Reflectivity ◽  
Horizontal Structure ◽  
Cell Patterns ◽  
Reflectivity Data

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The proposed method creates a 3D regular grid in which a horizontal size of meshes coincides with the horizontal model resolution. This minimizes the representative error associated with the discrepancy between resolutions of informational sources. After such preprocessing, horizontal structure functions and their gradients for radar reflectivity maintain the sizes and shapes of precipitation patterns similar to those of the original data. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. It redistributes precipitable water with smoothed values over the common domain since the control runs show, among several sub-domains with increased and decreased values, correspondingly. It also reproduces the mesoscale belts and cell patterns of sizes from a few to ten kilometers in precipitation fields. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 km to 6 km with major variations at 2.5 km to 3 km. It also reproduces the mesoscale belt and cell patterns of precipitation fields.

scholarly journals Assimilation of GNSS tomography products into WRF using radio occultation data assimilation operator

2019 ◽  
Author(s):  
Natalia Hanna ◽  
Estera Trzcina ◽  
Gregor Möller ◽  
Witold Rohm ◽  
Robert Weber
Keyword(s):  
Standard Deviation ◽  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Forecasting ◽  
Heavy Precipitation ◽  
Horizontal Resolution ◽  
Global Navigation Satellite Systems ◽  
Total Delay ◽  
Water Vapour Content ◽  
Precipitation Events

Abstract. From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a novel technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3D grid of voxels and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting – the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of Central Europe during the period of 29 May–14 June 2013, when heavy precipitation events were observed. For both models, Slant Wet Delays (SWD) were calculated based on estimates of Zenith Total Delay (ZTD) and horizontal gradients, provided for 72 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model). The GNSS tomography outputs have been assimilated into the nested (12- and 36-km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3DVar) system, in particular its radio occultation observations operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy precipitation events. Future improvements to the assimilation method are also discussed.

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