The Potential of Remote Sensing for Neutral Atmospheric Density Estimation in a Data Assimilation System

2005 ◽  
Vol 53 (4) ◽  
pp. 445-463
Author(s):  
C. F. Minter ◽  
T. J. Fuller-Rowell ◽  
M. V. Codrescu
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Density Estimation ◽  
Atmospheric Density ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

2020 ◽  
Vol 12 (24) ◽  
pp. 4018
Author(s):  
El houssaine Bouras ◽  
Lionel Jarlan ◽  
Salah Er-Raki ◽  
Clément Albergel ◽  
Bastien Richard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Time Scale ◽  
Condition Index ◽  
Cereal Production ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000–2017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.

scholarly journals Towards operational assimilation of surface based remote sensing temperature and humidity profiler data at MeteoSwiss

2021 ◽  
Author(s):  
Bas Crezee ◽  
Claire Merker ◽  
Jasmin Vural ◽  
Daniel Leuenberger ◽  
Alexander Haefele ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Weather Prediction ◽  
Good Representation ◽  
Raman Lidar ◽  
Temperature And Humidity ◽  
Microwave Radiometers ◽  
Data Assimilation System ◽  
Forward Operator ◽  
Assimilation System

<p>The current atmospheric observing systems fail to provide observations of temperature and humidity in the planetary boundary layer (PBL) with satisfactory spatial and temporal resolutions despite their potential positive impact on numerical weather prediction (NWP). This is particularly critical for humidity, which exhibits a very high variability in space and time, and for the vertical profile of temperature, which determines the atmospheric stability. Therefore, the analyzed thermodynamic structure of the PBL can be prone to errors, leading to poor forecasts of warnings for relevant phenomena, such as severe storms due to intense summer convection or winter fog and low stratus.</p><p>One approach to improve the model’s representation of the PBL is to include novel, ground-based remote sensing profiler observations in the data assimilation system to improve the forecast initial conditions. This also improves the quality of downstream applications relying on a good representation of the PBL in the model, such as dispersion modelling for emergency response after nuclear, chemical or biological incidents.</p><p>In this contribution, we present results of the MeteoSwiss effort to include observations from Raman lidar and microwave radiometers into the 1km mesh-size ensemble data assimilation system KENDA-1. To this end, we have developed a forward operator for water vapor mixing ratio and temperature to assimilate profiles from the Raman lidar. Brightness temperatures from the microwave radiometers are assimilated using the RTTOV-gb forward operator. We produced extensive O-B statistics to validate the observations with respect to the model and to derive the error covariance matrices of the observations. Furthermore, we will present results of several data assimilation cycling experiments during summer-time convective situations.</p>

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

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