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- Author or Editor: Fatima Karbou x
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Abstract
The aim of this study is to test the feasibility of assimilating microwave observations from the Advanced Microwave Sounding Units (AMSU-A and AMSU-B) through the implementation of an appropriate parameterization of sea ice emissivity. AMSU observations are relevant to the description of air temperature and humidity, and their assimilation into numerical weather prediction (NWP) helps better constrain models in regions where very few observations are assimilated. A sea ice emissivity model suitable for AMSU-A and AMSU-B data is described in this paper and its impact is studied through two assimilation experiments run during the period of the Arctic winter. The first experiment is representative of the operational version of the Météo-France NWP model whereas the second simulation uses the sea ice emissivity parameterization and assimilates a selection of AMSU channels above polar regions. The assimilation of AMSU observations over sea ice is shown to have a significant effect on atmospheric analyses (in particular those of temperature and humidity). The effect on temperature induces a warming in the lower troposphere, especially around 850 hPa. This leads to an increase in the Arctic inversion strength over the ice cap by almost 2 K. An improvement in medium-range forecasts is also noticed when the NWP model assimilates AMSU observations over sea ice.
Abstract
The aim of this study is to test the feasibility of assimilating microwave observations from the Advanced Microwave Sounding Units (AMSU-A and AMSU-B) through the implementation of an appropriate parameterization of sea ice emissivity. AMSU observations are relevant to the description of air temperature and humidity, and their assimilation into numerical weather prediction (NWP) helps better constrain models in regions where very few observations are assimilated. A sea ice emissivity model suitable for AMSU-A and AMSU-B data is described in this paper and its impact is studied through two assimilation experiments run during the period of the Arctic winter. The first experiment is representative of the operational version of the Météo-France NWP model whereas the second simulation uses the sea ice emissivity parameterization and assimilates a selection of AMSU channels above polar regions. The assimilation of AMSU observations over sea ice is shown to have a significant effect on atmospheric analyses (in particular those of temperature and humidity). The effect on temperature induces a warming in the lower troposphere, especially around 850 hPa. This leads to an increase in the Arctic inversion strength over the ice cap by almost 2 K. An improvement in medium-range forecasts is also noticed when the NWP model assimilates AMSU observations over sea ice.
Abstract
To improve the assimilation of Advanced Microwave Sounding Unit-A and -B (AMSU-A and -B) observations over land, three methods, based either on an estimation of the land emissivity or the land skin temperature directly from satellite observations, have been developed. Some feasibility studies have been performed in the Météo-France assimilation system in order to choose the most appropriate method for the system. This study reports on three 2-month assimilation and forecast experiments that use different methods to estimate AMSU-A and -B land emissivities together with the operational run as a control experiment. The experiments and the control have been subjected to several comparisons. The performance of the observation operator for simulating window channel brightness temperatures has been studied. The study shows considerable improvements in the statistics of the window channels’ first-guess departures (bias, standard deviation). The correlations between the observations and the model’s simulations have also been improved, especially over snow-covered areas. The performances of the assimilation system, in terms of cost function change, have been examined: the cost function is generally improved during the screening and remains stable during the minimization. Moreover, comparisons have been made in terms of impacts on both analyses and forecasts.
Abstract
To improve the assimilation of Advanced Microwave Sounding Unit-A and -B (AMSU-A and -B) observations over land, three methods, based either on an estimation of the land emissivity or the land skin temperature directly from satellite observations, have been developed. Some feasibility studies have been performed in the Météo-France assimilation system in order to choose the most appropriate method for the system. This study reports on three 2-month assimilation and forecast experiments that use different methods to estimate AMSU-A and -B land emissivities together with the operational run as a control experiment. The experiments and the control have been subjected to several comparisons. The performance of the observation operator for simulating window channel brightness temperatures has been studied. The study shows considerable improvements in the statistics of the window channels’ first-guess departures (bias, standard deviation). The correlations between the observations and the model’s simulations have also been improved, especially over snow-covered areas. The performances of the assimilation system, in terms of cost function change, have been examined: the cost function is generally improved during the screening and remains stable during the minimization. Moreover, comparisons have been made in terms of impacts on both analyses and forecasts.
Abstract
Surface emissivities computed at 89 GHz from AMSU-A, AMSU-B, and SSMI/S instruments are used to detect rain events and to estimate a daily precipitation rate over land surfaces. This new retrieval algorithm, called the emissivity rainfall retrieval (EMIRR) algorithm, is evaluated over France and compared with several other precipitation products. The precipitation detection is performed using temporal changes in daily surface emissivities. A statistical fit, derived from a rainfall analysis product using rain gauge and radar data, is devised to estimate a daily precipitation rate from surface emissivities. Rain retrievals are evaluated over a 1-yr period in 2010 against other precipitation products, including rain gauge measurements. The EMIRR algorithm allows a reasonable detection of rainy events from daily surface emissivities. The number of rainy days and the daily rainfall rates compare well to estimates from other precipitation products. However, the algorithm tends to overestimate low precipitation amounts and to underestimate higher ones, with reduced performances in the presence of snow. Despite such limitations, this new method is very promising and provides a demonstration of the potential use of the 89-GHz surface emissivities to infer relevant information (occurrence and amounts) related to daily precipitation over land surfaces.
Abstract
Surface emissivities computed at 89 GHz from AMSU-A, AMSU-B, and SSMI/S instruments are used to detect rain events and to estimate a daily precipitation rate over land surfaces. This new retrieval algorithm, called the emissivity rainfall retrieval (EMIRR) algorithm, is evaluated over France and compared with several other precipitation products. The precipitation detection is performed using temporal changes in daily surface emissivities. A statistical fit, derived from a rainfall analysis product using rain gauge and radar data, is devised to estimate a daily precipitation rate from surface emissivities. Rain retrievals are evaluated over a 1-yr period in 2010 against other precipitation products, including rain gauge measurements. The EMIRR algorithm allows a reasonable detection of rainy events from daily surface emissivities. The number of rainy days and the daily rainfall rates compare well to estimates from other precipitation products. However, the algorithm tends to overestimate low precipitation amounts and to underestimate higher ones, with reduced performances in the presence of snow. Despite such limitations, this new method is very promising and provides a demonstration of the potential use of the 89-GHz surface emissivities to infer relevant information (occurrence and amounts) related to daily precipitation over land surfaces.
Abstract
The Concordiasi field experiment, which is taking place in Antarctica, involves the launching of radiosoundings and stratospheric balloons. One of the main goals of this campaign is the validation of the Infrared Atmospheric Sounding Interferometer (IASI) radiance assimilation. Prior to the campaign, it was necessary to improve satellite data assimilation at high latitudes. Two types of sensors, microwave and infrared, have been considered to help with this issue. A major problem associated with microwave satellite data is the calculation of the surface emissivity. An innovative approach, based on satellite observations, improves the surface emissivity modeling over land and sea ice within the constraints of the four-dimensional variational data assimilation (4D-VAR) system. With this new calculation of emissivity, it has been possible to include many more microwave observations during the assimilation. In this study, this method has been applied to high latitudes, after some adjustments have been made to assimilate additional Advanced Microwave Sounding Unit-A/B (AMSU-A/B) data over sea ice and snow. The use of additional data from IASI and the Atmospheric Infrared Sounder (AIRS) sensors over land and sea ice has also been tested. The use of the microwave and infrared data over this polar area has modified the dynamical and thermodynamical model fields such as the snow precipitation quantity. Additional data have been found to have a positive impact on the skill of a model specially tuned for Antarctica.
Abstract
The Concordiasi field experiment, which is taking place in Antarctica, involves the launching of radiosoundings and stratospheric balloons. One of the main goals of this campaign is the validation of the Infrared Atmospheric Sounding Interferometer (IASI) radiance assimilation. Prior to the campaign, it was necessary to improve satellite data assimilation at high latitudes. Two types of sensors, microwave and infrared, have been considered to help with this issue. A major problem associated with microwave satellite data is the calculation of the surface emissivity. An innovative approach, based on satellite observations, improves the surface emissivity modeling over land and sea ice within the constraints of the four-dimensional variational data assimilation (4D-VAR) system. With this new calculation of emissivity, it has been possible to include many more microwave observations during the assimilation. In this study, this method has been applied to high latitudes, after some adjustments have been made to assimilate additional Advanced Microwave Sounding Unit-A/B (AMSU-A/B) data over sea ice and snow. The use of additional data from IASI and the Atmospheric Infrared Sounder (AIRS) sensors over land and sea ice has also been tested. The use of the microwave and infrared data over this polar area has modified the dynamical and thermodynamical model fields such as the snow precipitation quantity. Additional data have been found to have a positive impact on the skill of a model specially tuned for Antarctica.
A 1D-Var Approach to Retrieve Clear-Sky Wet Tropospheric Correction from Current and Future Altimetry MissionsAbstract
Statistical methods are usually used to provide estimations of the wet tropospheric correction (WTC), necessary to correct altimetry measurements for atmospheric path delays, using brightness temperatures measured at two or three low frequencies from a passive microwave radiometer on board the altimeter mission. Despite their overall accuracy over oceanic surfaces, uncertainties still remain in specific regions of complex atmospheric stratification. Thus, there is still a need to improve the methods currently used by taking into account the frequency-dependent information content of the observations and the atmospheric and surface variations in the surroundings of the observations. In this article we focus on the assimilation of relevant passive microwave observations to retrieve the WTC over ocean using different altimeter mission contexts (current and future, providing brightness temperature measurements at higher frequencies in addition to classical low frequencies). Data assimilation is performed using a one-dimensional variational data assimilation (1D-Var) method. The behavior of the 1D-Var is evaluated by verifying its physical consistency when using pseudo- and real observations. Several observing-system simulation experiments are run and their results are analyzed to evaluate global and regional WTC retrievals. Comparisons of 1D-Var-based TWC retrieval and reference products from classical WTC retrieval algorithms or radio-occultation data are also performed to assess the 1D-Var performances.
Abstract
Statistical methods are usually used to provide estimations of the wet tropospheric correction (WTC), necessary to correct altimetry measurements for atmospheric path delays, using brightness temperatures measured at two or three low frequencies from a passive microwave radiometer on board the altimeter mission. Despite their overall accuracy over oceanic surfaces, uncertainties still remain in specific regions of complex atmospheric stratification. Thus, there is still a need to improve the methods currently used by taking into account the frequency-dependent information content of the observations and the atmospheric and surface variations in the surroundings of the observations. In this article we focus on the assimilation of relevant passive microwave observations to retrieve the WTC over ocean using different altimeter mission contexts (current and future, providing brightness temperature measurements at higher frequencies in addition to classical low frequencies). Data assimilation is performed using a one-dimensional variational data assimilation (1D-Var) method. The behavior of the 1D-Var is evaluated by verifying its physical consistency when using pseudo- and real observations. Several observing-system simulation experiments are run and their results are analyzed to evaluate global and regional WTC retrievals. Comparisons of 1D-Var-based TWC retrieval and reference products from classical WTC retrieval algorithms or radio-occultation data are also performed to assess the 1D-Var performances.
Abstract
This study describes the work performed at the European Centre for Medium-Range Weather Forecasts (ECMWF) to estimate the microwave land surface emissivities at Advanced Microwave Sounding Unit (AMSU)-A frequencies within the specific context and constraint of operational assimilation. The emissivities are directly calculated from the satellite observations in clear-sky conditions using the surface skin temperature derived from ECMWF and the Radiative Transfer for the Television and Infrared Observation Satellite Operational Vertical Sounder (RTTOVS) model, along with the forecast model variables to estimate the atmospheric contributions. The results are analyzed, with special emphasis on the evaluation of the frequency and angular dependencies of the emissivities with respect to the surface characteristics. Possible extrapolation of the Special Sensor Microwave Imager (SSM/I) emissivities to those of the AMSU is considered. Direct calculation results are also compared with emissivity model outputs.
Abstract
This study describes the work performed at the European Centre for Medium-Range Weather Forecasts (ECMWF) to estimate the microwave land surface emissivities at Advanced Microwave Sounding Unit (AMSU)-A frequencies within the specific context and constraint of operational assimilation. The emissivities are directly calculated from the satellite observations in clear-sky conditions using the surface skin temperature derived from ECMWF and the Radiative Transfer for the Television and Infrared Observation Satellite Operational Vertical Sounder (RTTOVS) model, along with the forecast model variables to estimate the atmospheric contributions. The results are analyzed, with special emphasis on the evaluation of the frequency and angular dependencies of the emissivities with respect to the surface characteristics. Possible extrapolation of the Special Sensor Microwave Imager (SSM/I) emissivities to those of the AMSU is considered. Direct calculation results are also compared with emissivity model outputs.
Abstract
Estimating the impact of wind-driven snow transport requires modeling wind fields with a lower grid spacing than the spacing on the order of 1 or a few kilometers used in the current numerical weather prediction (NWP) systems. In this context, we introduce a new strategy to downscale wind fields from NWP systems to decametric scales, using high-resolution (30 m) topographic information. Our method (named “DEVINE”) is leveraged on a convolutional neural network (CNN), trained to replicate the behavior of the complex atmospheric model ARPS, and was previously run on a large number (7279) of synthetic Gaussian topographies under controlled weather conditions. A 10-fold cross validation reveals that our CNN is able to accurately emulate the behavior of ARPS (mean absolute error for wind speed = 0.16 m s−1). We then apply DEVINE to real cases in the Alps, that is, downscaling wind fields forecast by the AROME NWP system using information from real alpine topographies. DEVINE proved able to reproduce main features of wind fields in complex terrain (acceleration on ridges, leeward deceleration, and deviations around obstacles). Furthermore, an evaluation on quality-checked observations acquired at 61 sites in the French Alps reveals improved behavior of the downscaled winds (AROME wind speed mean bias is reduced by 27% with DEVINE), especially at the most elevated and exposed stations. Wind direction is, however, only slightly modified. Hence, despite some current limitations inherited from the ARPS simulations setup, DEVINE appears to be an efficient downscaling tool whose minimalist architecture, low input data requirements (NWP wind fields and high-resolution topography), and competitive computing times may be attractive for operational applications.
Significance Statement
Wind largely influences the spatial distribution of snow in mountains, with direct consequences on hydrology and avalanche hazard. Most operational models predicting wind in complex terrain use a grid spacing on the order of several kilometers, too coarse to represent the real patterns of mountain winds. We introduce a novel method based on deep learning to increase this spatial resolution while maintaining acceptable computational costs. Our method mimics the behavior of a complex model that is able to represent part of the complexity of mountain winds by using topographic information only. We compared our results with observations collected in complex terrain and showed that our model improves the representation of winds, notably at the most elevated and exposed observation stations.
Abstract
Estimating the impact of wind-driven snow transport requires modeling wind fields with a lower grid spacing than the spacing on the order of 1 or a few kilometers used in the current numerical weather prediction (NWP) systems. In this context, we introduce a new strategy to downscale wind fields from NWP systems to decametric scales, using high-resolution (30 m) topographic information. Our method (named “DEVINE”) is leveraged on a convolutional neural network (CNN), trained to replicate the behavior of the complex atmospheric model ARPS, and was previously run on a large number (7279) of synthetic Gaussian topographies under controlled weather conditions. A 10-fold cross validation reveals that our CNN is able to accurately emulate the behavior of ARPS (mean absolute error for wind speed = 0.16 m s−1). We then apply DEVINE to real cases in the Alps, that is, downscaling wind fields forecast by the AROME NWP system using information from real alpine topographies. DEVINE proved able to reproduce main features of wind fields in complex terrain (acceleration on ridges, leeward deceleration, and deviations around obstacles). Furthermore, an evaluation on quality-checked observations acquired at 61 sites in the French Alps reveals improved behavior of the downscaled winds (AROME wind speed mean bias is reduced by 27% with DEVINE), especially at the most elevated and exposed stations. Wind direction is, however, only slightly modified. Hence, despite some current limitations inherited from the ARPS simulations setup, DEVINE appears to be an efficient downscaling tool whose minimalist architecture, low input data requirements (NWP wind fields and high-resolution topography), and competitive computing times may be attractive for operational applications.
Significance Statement
Wind largely influences the spatial distribution of snow in mountains, with direct consequences on hydrology and avalanche hazard. Most operational models predicting wind in complex terrain use a grid spacing on the order of several kilometers, too coarse to represent the real patterns of mountain winds. We introduce a novel method based on deep learning to increase this spatial resolution while maintaining acceptable computational costs. Our method mimics the behavior of a complex model that is able to represent part of the complexity of mountain winds by using topographic information only. We compared our results with observations collected in complex terrain and showed that our model improves the representation of winds, notably at the most elevated and exposed observation stations.
Abstract
The Crocus snowpack model within the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model was run over northern Eurasia from 1979 to 1993, using forcing data extracted from hydrometeorological datasets and meteorological reanalyses. Simulated snow depth, snow water equivalent, and density over open fields were compared with local observations from over 1000 monitoring sites, available either once a day or three times per month. The best performance is obtained with European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Provided blowing snow sublimation is taken into account, the simulations show a small bias and high correlations in terms of snow depth, snow water equivalent, and density. Local snow cover durations as well as the onset and vanishing dates of continuous snow cover are also well reproduced. A major result is that the overall performance of the simulations is very similar to the performance of existing gridded snow products, which, in contrast, assimilate local snow depth observations. Soil temperature at 20-cm depth is reasonably well simulated. The methodology developed in this study is an efficient way to evaluate different meteorological datasets, especially in terms of snow precipitation. It reveals that the temporal disaggregation of monthly precipitation in the hydrometeorological dataset from Princeton University significantly impacts the rain–snow partitioning, deteriorating the simulation of the onset of snow cover as well as snow depth throughout the cold season.
Abstract
The Crocus snowpack model within the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model was run over northern Eurasia from 1979 to 1993, using forcing data extracted from hydrometeorological datasets and meteorological reanalyses. Simulated snow depth, snow water equivalent, and density over open fields were compared with local observations from over 1000 monitoring sites, available either once a day or three times per month. The best performance is obtained with European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Provided blowing snow sublimation is taken into account, the simulations show a small bias and high correlations in terms of snow depth, snow water equivalent, and density. Local snow cover durations as well as the onset and vanishing dates of continuous snow cover are also well reproduced. A major result is that the overall performance of the simulations is very similar to the performance of existing gridded snow products, which, in contrast, assimilate local snow depth observations. Soil temperature at 20-cm depth is reasonably well simulated. The methodology developed in this study is an efficient way to evaluate different meteorological datasets, especially in terms of snow precipitation. It reveals that the temporal disaggregation of monthly precipitation in the hydrometeorological dataset from Princeton University significantly impacts the rain–snow partitioning, deteriorating the simulation of the onset of snow cover as well as snow depth throughout the cold season.
Abstract
Observations from Advanced Microwave Sounding Unit-A and -B (AMSU-A and -B) have been more intensively used over sea than over land because of large uncertainties about the land surface emissivity and the skin temperature. Several methods based on a direct estimation of the land emissivity from satellite observations have been found to be very useful for improving the assimilation of sounding channels over land. Feasibility studies have been conducted within the Météo-France global assimilation system in order to examine the possibility of assimilating low-level atmospheric observations receiving a contribution from the land surface. The present study reports on three 2-month assimilation and forecast experiments, which include the assimilation of surface-sensitive observations from AMSU-A and -B together with a control experiment, which represents the operational model. The assimilation experiments have been compared with the control, and important changes in the analyzed atmospheric fields and in the precipitation forecasts over parts of the tropics, and especially over West Africa, have been noticed. The experiments seem to emphasize the atmospheric moistening in India, South America, and in West Africa, together with atmospheric drying over Saudi Arabia and northeast Africa. The drying or moistening of the atmosphere has been successfully evaluated using independent measurements from the GPS African Monsoon Multidisciplinary Analysis (AMMA) network. Precipitation and OLR forecasts have also been examined and compared with independent measurements. Physically, the changes result in a better-organized African monsoon with a stronger ITCZ in terms of ascent, vorticity, and precipitation, but there is no northward shift of the monsoon system. Low-level humidity observations have been found to have important impacts on the analysis and to produce positive impacts on forecast scores over the tropics.
Abstract
Observations from Advanced Microwave Sounding Unit-A and -B (AMSU-A and -B) have been more intensively used over sea than over land because of large uncertainties about the land surface emissivity and the skin temperature. Several methods based on a direct estimation of the land emissivity from satellite observations have been found to be very useful for improving the assimilation of sounding channels over land. Feasibility studies have been conducted within the Météo-France global assimilation system in order to examine the possibility of assimilating low-level atmospheric observations receiving a contribution from the land surface. The present study reports on three 2-month assimilation and forecast experiments, which include the assimilation of surface-sensitive observations from AMSU-A and -B together with a control experiment, which represents the operational model. The assimilation experiments have been compared with the control, and important changes in the analyzed atmospheric fields and in the precipitation forecasts over parts of the tropics, and especially over West Africa, have been noticed. The experiments seem to emphasize the atmospheric moistening in India, South America, and in West Africa, together with atmospheric drying over Saudi Arabia and northeast Africa. The drying or moistening of the atmosphere has been successfully evaluated using independent measurements from the GPS African Monsoon Multidisciplinary Analysis (AMMA) network. Precipitation and OLR forecasts have also been examined and compared with independent measurements. Physically, the changes result in a better-organized African monsoon with a stronger ITCZ in terms of ascent, vorticity, and precipitation, but there is no northward shift of the monsoon system. Low-level humidity observations have been found to have important impacts on the analysis and to produce positive impacts on forecast scores over the tropics.
Abstract
A one-dimensional variational data assimilation (1DVar) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain gauges and precipitation estimates from weather radars with background information from short-range numerical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar model allows an effective assimilation of measurements of different types, including rain gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.
Abstract
A one-dimensional variational data assimilation (1DVar) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain gauges and precipitation estimates from weather radars with background information from short-range numerical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar model allows an effective assimilation of measurements of different types, including rain gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.
