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 Impact of airborne cloud radar reflectivity data assimilation on kilometre-scale numerical weather prediction analyses and forecasts of heavy precipitation events

2019 ◽  
Vol 19 (4) ◽  
pp. 907-926 ◽  
Author(s):  
Mary Borderies ◽  
Olivier Caumont ◽  
Julien Delanoë ◽  
Véronique Ducrocq ◽  
Nadia Fourrié ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Mediterranean Area ◽  
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 numerical weather prediction (NWP) model AROME, has been adapted to assimilate the W-band reflectivity measured by the airborne cloud radar RASTA (Radar Airborne System Tool for Atmosphere) during a 2-month period over the Mediterranean area. After applying a bias correction, vertical profiles of relative humidity are first derived via a 1-D Bayesian retrieval, and then used as relative humidity pseudo-observations in the 3DVar assimilation system of AROME. The efficiency of the 1-D 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 separately and jointly assimilating the W-band reflectivity and horizontal wind measurements collected by RASTA in the 3 h 3DVar assimilation system of AROME. The data assimilation experiments are conducted for a single heavy precipitation event and then also 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 when compared to the assimilation of RASTA wind data alone. Generally, the joint assimilation of the W-band reflectivity with wind data results in the best improvement in 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 lesser extent, the assimilation of the W-band reflectivity alone also results in a slight improvement of the rainfall precipitation forecasts.

scholarly journals Assimilation of wind data from airborne Doppler cloud-profiling radar in a kilometre-scale NWP system

2018 ◽  
Author(s):  
Mary Borderies ◽  
Olivier Caumont ◽  
Julien Delanoë ◽  
Véronique Ducrocq ◽  
Nadia Fourrié
Keyword(s):  
Positive Impact ◽  
Heavy Precipitation ◽  
Mediterranean Area ◽  
Western Mediterranean ◽  
Wind Data ◽  
Heavy Rainfall Event ◽  
Rain Gauges ◽  
The North ◽  
W Band ◽  
The Impact

Abstract. The article reports on the impact of the assimilation of wind vertical profile data in a kilometre-scale NWP system on predicting heavy precipitation events in the north-western Mediterranean area. The data collected in diverse conditions by the airborne W-band radar RASTA (Radar Airborne System Tool for Atmosphere) during a 45-day period are assimilated in the 3-h 3DVar assimilation system of AROME. The impact of the length of the assimilation window is investigated. The data assimilation experiments are performed for a heavy rainfall event, which occurred over south-eastern France on 26 September 2012 (IOP7a), and over a 45-day cycled period. During IOP7a, results indicate that the quality of the rainfall accumulation forecasts increases with the length of the assimilation window. By contrast, on the 45-day period, the best scores against rain gauges measurements are reached with a 1 hour assimilation window, which recommends to use observations with a small period centred on the assimilation time. The positive impact of the assimilation of RASTA wind data is particularly evidenced for the IOP7a case since results indicate an improvement in the predicted wind at short term ranges (2 hours and 3 hours) and in the 12-hour precipitation forecasts. However, on the 45-day cycled period, the comparison against other assimilated observations shows an overall neutral impact. Results are still encouraging since a slight positive improvement in the 6-, 9- and 12-hour precipitation forecasts of heavier rainfall was demonstrated.

scholarly journals Assimilation of wind data from airborne Doppler cloud-profiling radar in a kilometre-scale NWP system

2019 ◽  
Vol 19 (4) ◽  
pp. 821-835
Author(s):  
Mary Borderies ◽  
Olivier Caumont ◽  
Julien Delanoë ◽  
Véronique Ducrocq ◽  
Nadia Fourrié
Keyword(s):  
Positive Impact ◽  
Heavy Precipitation ◽  
Mediterranean Area ◽  
Western Mediterranean ◽  
Wind Data ◽  
Heavy Rainfall Event ◽  
The North ◽  
W Band ◽  
Large Period ◽  
The Impact

Abstract. The article reports on the impact of the assimilation of wind vertical profile data in a kilometre-scale NWP system on predicting heavy precipitation events in the north-western Mediterranean area. The data collected in diverse conditions by the airborne W-band radar RASTA (Radar Airborne System Tool for Atmosphere) during a 45-day period are assimilated in the 3 h 3DVAR assimilation system of AROME. The impact of the length of the assimilation window is investigated. The data assimilation experiments are performed for a heavy rainfall event, which occurred over south-eastern France on 26 September 2012 (IOP7a) and over a 45-day cycled period. Results indicate that the quality of the rainfall accumulation forecasts increases with the length of the assimilation window, which recommends using observations with a large period centred on the assimilation time. The positive impact of the assimilation of RASTA wind data is particularly evidenced for the IOP7a case since results indicate an improvement in the predicted wind at short-term ranges (2 and 3 h) and in the 11 h precipitation forecasts. However, in the 45-day cycled period, the comparison against other assimilated observations shows an overall neutral impact. Results are still encouraging since a slight positive improvement in the 5, 8 and 11 h precipitation forecasts was demonstrated.

scholarly journals Implementation of a 3-D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

2013 ◽  
Vol 6 (2) ◽  
pp. 3581-3610
Author(s):  
S. Federico
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wind Profiler ◽  
Short Term ◽  
Recursive Filters ◽  
Data Assimilation System ◽  
Rams Model ◽  
Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system. The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost-function, and the use of an analysis space represented by recursive filters and eigenmodes of the vertical background error matrix. This matrix and the length-scale of the recursive filters are estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analysis solutions because they are closer to the observations (lower RMSE) compared to the background (higher RMSE), and the differences of the RMSEs are consistent with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of a three-hours forecast of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3-D-Var scheme as initial conditions, then is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at a-synoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range (1–2 h). The results are in agreement with the set-up of the numerical experiment.

Simulation of  W‐band radar reflectivity for model validation and data assimilation

2018 ◽  
Vol 144 (711) ◽  
pp. 391-403 ◽  
Author(s):  
M. Borderies ◽  
O. Caumont ◽  
C. Augros ◽  
É. Bresson ◽  
J. Delanoë ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Model Validation ◽  
Radar Reflectivity ◽  
W Band

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 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 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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