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 Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

2021 ◽  
Author(s):  
Magnus Lindskog ◽  
Adam Dybbroe ◽  
Roger Randriamampianina
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Implementation Process ◽  
Satellite System ◽  
Limited Area ◽  
Global Navigation Satellite ◽  
Microwave Radiances ◽  
Data Assimilation System ◽  
Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

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.

scholarly journals SINGV‐DA: A data assimilation system for convective‐scale numerical weather prediction over Singapore

2020 ◽  
Vol 146 (729) ◽  
pp. 1923-1938 ◽  
Author(s):  
B. C. Peter Heng ◽  
Robert Tubbs ◽  
Xiang‐Yu Huang ◽  
Bruce Macpherson ◽  
Dale M. Barker ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Convective Scale ◽  
Data Assimilation System ◽  
Assimilation System

Forecast Impact of Assimilating Aircraft WVSS-II Water Vapor Mixing Ratio Observations in the Global Data Assimilation System (GDAS)

2017 ◽  
Vol 32 (4) ◽  
pp. 1603-1611 ◽  
Author(s):  
Brett T. Hoover ◽  
David A. Santek ◽  
Anne-Sophie Daloz ◽  
Yafang Zhong ◽  
Richard Dworak ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Global Data Assimilation System ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

Abstract Automated aircraft observations of wind and temperature have demonstrated positive impact on numerical weather prediction since the mid-1980s. With the advent of the Water Vapor Sensing System (WVSS-II) humidity sensor, the expanding fleet of commercial aircraft with onboard automated sensors is also capable of delivering high quality moisture observations, providing vertical profiles of moisture as aircraft ascend out of and descend into airports across the continental United States. Observations from the WVSS-II have to date only been monitored within the Global Data Assimilation System (GDAS) without being assimilated. In this study, aircraft moisture observations from the WVSS-II are assimilated into the GDAS, and their impact is assessed in the Global Forecast System (GFS). A two-season study is performed, demonstrating a statistically significant positive impact on both the moisture forecast and the precipitation forecast at short range (12–36 h) during the warm season. No statistically significant impact is observed during the cold season.

A Velocity Dealiasing Technique Using Rapidly Updated Analysis from a Four-Dimensional Variational Doppler Radar Data Assimilation System

2010 ◽  
Vol 27 (7) ◽  
pp. 1140-1152 ◽  
Author(s):  
Eunha Lim ◽  
Juanzhen Sun
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Radar Data ◽  
Objective Analysis ◽  
Doppler Velocity ◽  
Horizontal Shear ◽  
Data Assimilation System ◽  
Radar Data Assimilation ◽  
Assimilation System

Abstract A Doppler velocity dealiasing algorithm is developed within the storm-scale four-dimensional radar data assimilation system known as the Variational Doppler Radar Analysis System (VDRAS). The innovative aspect of the algorithm is that it dealiases Doppler velocity at each grid point independently by using three-dimensional wind fields obtained either from an objective analysis using conventional observations and mesoscale model output or from a rapidly updated analysis of VDRAS that assimilates radar data. This algorithm consists of three steps: preserving horizontal shear, global dealiasing using reference wind from the objective analysis or the VDRAS analysis, and local dealiasing. It is automated and intended to be used operationally for radar data assimilation using numerical weather prediction models. The algorithm was tested with 384 volumes of radar data observed from the Next Generation Weather Radar (NEXRAD) for a severe thunderstorm that occurred during 15 June 2002. It showed that the algorithm was effective in dealiasing large areas of aliased velocities when the wind from the objective analysis was used as the reference and that more accurate dealiasing was achieved by using the continuously cycled VDRAS analysis.

scholarly journals Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system

2014 ◽  
Vol 7 (11) ◽  
pp. 11927-11956 ◽  
Author(s):  
H. Kwon ◽  
J.-S. Kang ◽  
Y. Jo ◽  
J. H. Kang
Keyword(s):  
Quality Control ◽  
Data Assimilation ◽  
Data Processing ◽  
Weather Prediction ◽  
Processing System ◽  
Data Processing System ◽  
Bending Angle ◽  
Quality Control Process ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a new global numerical weather prediction model and an advanced data assimilation system. As part of the KIAPS Package for Observation Processing (KPOP) system for data assimilation, preprocessing and quality control modules for bending angle measurements of global positioning system radio occultation (GPS-RO) data have been implemented and examined. GPS-RO data processing system is composed of several steps for checking observation locations, missing values, physical values for Earth radius of curvature, and geoid undulation. An observation-minus-background check is implemented by use of a one-dimensional observational bending angle operator and tangent point drift is also considered in the quality control process. We have tested GPS-RO observations utilized by the Korean Meteorological Administration (KMA) within KPOP, based on both the KMA global model and the National Center for Atmospheric Research (NCAR) Community Atmosphere Model-Spectral Element (CAM-SE) as a model background. Background fields from the CAM-SE model are incorporated for the preparation of assimilation experiments with the KIAPS-LETKF data assimilation system, which has been successfully implemented to a cubed-sphere model with fully unstructured quadrilateral meshes. As a result of data processing, the bending angle departure statistics between observation and background shows significant improvement. Also, the first experiment in assimilating GPS-RO bending angle resulting from KPOP within KIAPS-LETKF shows encouraging results.

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.

Assimilation of Global Positioning System Radio Occultation Observations into NCEP’s Global Data Assimilation System

2007 ◽  
Vol 135 (9) ◽  
pp. 3174-3193 ◽  
Author(s):  
L. Cucurull ◽  
J. C. Derber ◽  
R. Treadon ◽  
R. J. Purser
Keyword(s):  
Quality Control ◽  
Data Assimilation ◽  
Real Time ◽  
Radio Occultation ◽  
Environmental Modeling ◽  
Bending Angle ◽  
Preliminary Results ◽  
Data Assimilation System ◽  
Forward Operator ◽  
Assimilation System

Abstract The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched six small satellites in April 2006, each carrying a GPS radio occultation (RO) receiver. At final orbit, COSMIC will provide ∼2500–3000 RO soundings per day uniformly distributed around the globe in near–real time. In preparation for the assimilation of COSMIC data in an operational framework, the NCEP/Environmental Modeling Center (EMC) has successfully developed the capability of assimilating profiles of refractivity and bending angle. Each forward operator has been implemented with its own quality control and error characterization. In this paper, the infrastructure developed at NCEP/EMC to assimilate GPS RO observations, including forward models, observational and representativeness errors, and quality control procedures, is described. The advantages of using a forward operator for bending angle versus refractivity are discussed and some preliminary results on the benefits of the GPS RO in weather analysis and forecasts are presented. The different strategies adopted at NCEP/EMC to assimilate GPS RO data are aimed to select the most appropriate forward operator in the operational data assimilation system when COSMIC products are stable and routinely available to the Numerical Weather Centers. In the meantime, data from the Challenging Minisatellite Payload (CHAMP) satellite is available in non–real time and has been used in the assimilation tests to examine the potential benefits of the GPS RO–derived products. In the preliminary results presented in this study, the use of GPS RO observations slightly improves anomaly correlation scores for temperature (by ∼0.01–0.03) in the Southern Hemisphere and Tropics throughout the depth of the atmosphere while a slight degradation is found in the upper troposphere and stratosphere in the Northern Hemisphere. However, significant reduction of the temperature and humidity biases is found for all latitudes. The benefits from assimilating GPS RO data also extend to other fields, such as 500-hPa geopotential heights and tropical winds, demonstrating the potential use of GPS RO data in operational forecasting.

Sign in / Sign up

Export Citation Format

Share Document