Estimates of observation-error characteristics in clear and cloudy regions for microwave imager radiances from numerical weather prediction

2011 ◽  
Vol 137 (661) ◽  
pp. 2014-2023 ◽  
Author(s):  
Niels Bormann ◽  
Alan J. Geer ◽  
Peter Bauer
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Observation Error ◽  
Numerical Weather ◽  
Error Characteristics ◽  
Microwave Imager

scholarly journals Update of Infrared Atmospheric Sounding Interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP)

2020 ◽  
Vol 13 (5) ◽  
pp. 2659-2680
Author(s):  
Olivier Coopmann ◽  
Vincent Guidard ◽  
Nadia Fourrié ◽  
Béatrice Josse ◽  
Virginie Marécal
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Channel Selection ◽  
Observation Error ◽  
Numerical Weather ◽  
Atmospheric Sounding ◽  
Analysis Error ◽  
Concept Of Information ◽  
Selection Of

Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is an essential instrument for numerical weather prediction (NWP). It measures radiances at the top of the atmosphere using 8461 channels. The huge amount of observations provided by IASI has led the community to develop techniques to reduce observations while conserving as much information as possible. Thus, a selection of the 300 most informative channels was made for NWP based on the concept of information theory. One of the main limitations of this method was to neglect the covariances between the observation errors of the different channels. However, many centres have shown a significant benefit for weather forecasting to use them. Currently, the observation-error covariances are only estimated on the current IASI channel selection, but no studies to make a new selection of IASI channels taking into account the observation-error covariances have yet been carried out. The objective of this paper was therefore to perform a new selection of IASI channels by taking into account the observation-error covariances. The results show that with an equivalent number of channels, accounting for the observation-error covariances, a new selection of IASI channels can reduce the analysis error on average in temperature by 3 %, humidity by 1.8 % and ozone by 0.9 % compared to the current selection. Finally, we go one step further by proposing a robust new selection of 400 IASI channels to further reduce the analysis error for NWP.

scholarly journals Impact of Doppler Radar Wind Observations on Australian High-Resolution Numerical Weather Prediction

2020 ◽  
Vol 35 (2) ◽  
pp. 309-324
Author(s):  
Susan Rennie ◽  
Lawrence Rikus ◽  
Nathan Eizenberg ◽  
Peter Steinle ◽  
Monika Krysta
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Limited Area ◽  
Observation Error ◽  
Numerical Weather ◽  
Skill Scores ◽  
The Impact

Abstract The impact of Doppler radar wind observations on forecasts from a developmental, high-resolution numerical weather prediction (NWP) system is assessed. The new 1.5-km limited-area model will be Australia’s first such operational NWP system to include data assimilation. During development, the assimilation of radar wind observations was trialed over a 2-month period to approve the initial inclusion of these observations. Three trials were run: the first with no radar data, the second with radial wind observations from precipitation echoes, and the third with radial winds from both precipitation and insect echoes. The forecasts were verified against surface observations from automatic weather stations, against rainfall accumulations using fractions skill scores, and against satellite cloud observations. These methods encompassed verification across a range of vertical levels. Additionally, a case study was examined more closely. Overall results showed little statistical difference in skill between the trials, and the net impact was neutral. While the new observations clearly affected the forecast, the objective and subjective analyses showed a neutral impact on the forecast overall. As a first step, this result is satisfactory for the operational implementation. In future, upgrades to the radar network will start to reduce the observation error, and further improvements to the data assimilation are planned, which may be expected to improve the impact.

scholarly journals Assessment of New Satellite Missions within the Framework of Numerical Weather Prediction

2020 ◽  
Vol 12 (10) ◽  
pp. 1580 ◽  
Author(s):  
Stuart Newman ◽  
Fabien Carminati ◽  
Heather Lawrence ◽  
Niels Bormann ◽  
Kirsti Salonen ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Gap Analysis ◽  
Weather Prediction ◽  
Data Sets ◽  
Next Generation ◽  
Numerical Weather ◽  
Surface Emission ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Global Precipitation

Confidence in the use of Earth observations for monitoring essential climate variables (ECVs) relies on the validation of satellite calibration accuracy to within a well-defined uncertainty. The gap analysis for integrated atmospheric ECV climate monitoring (GAIA-CLIM) project investigated the calibration/validation of satellite data sets using non-satellite reference data. Here, we explore the role of numerical weather prediction (NWP) frameworks for the assessment of several meteorological satellite sensors: the advanced microwave scanning radiometer 2 (AMSR2), microwave humidity sounder-2 (MWHS-2), microwave radiation imager (MWRI), and global precipitation measurement (GPM) microwave imager (GMI). We find departures (observation-model differences) are sensitive to instrument calibration artefacts. Uncertainty in surface emission is identified as a key gap in our ability to validate microwave imagers quantitatively in NWP. The prospects for NWP-based validation of future instruments are considered, taking as examples the microwave sounder (MWS) and infrared atmospheric sounding interferometer-next generation (IASI-NG) on the next generation of European polar-orbiting satellites. Through comparisons with reference radiosondes, uncertainties in NWP fields can be estimated in terms of equivalent top-of-atmosphere brightness temperature. We find NWP-sonde differences are consistent with a total combined uncertainty of 0.15 K for selected temperature sounding channels, while uncertainties for humidity sounding channels typically exceed 1 K.

scholarly journals Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome

2021 ◽  
Vol 14 (9) ◽  
pp. 5925-5938
Author(s):  
Susanna Hagelin ◽  
Roohollah Azad ◽  
Magnus Lindskog ◽  
Harald Schyberg ◽  
Heiner Körnich
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Limited Area ◽  
Horizontal Line ◽  
Observation Error ◽  
Numerical Weather ◽  
Potential Improvement ◽  
Nwp Model ◽  
The Impact

Abstract. The impact of using wind observations from the Aeolus satellite in a limited-area numerical weather prediction (NWP) system is being investigated using the limited-area NWP model Harmonie–Arome over the Nordic region. We assimilate the horizontal line-of-sight (HLOS) winds observed by Aeolus using 3D-Var data assimilation for two different periods, one in September–October 2018 when the satellite was recently launched and a later period in April–May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations has degraded between the first and second experiment period over our domain. However, observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods, whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation, which generates flow-dependent analysis increments, with the Aeolus data.

scholarly journals Assessments of F16 Special Sensor Microwave Imager and Sounder Antenna Temperatures at Lower Atmospheric Sounding Channels

2009 ◽  
Vol 2009 ◽  
pp. 1-18 ◽  
Author(s):  
Banghua Yan ◽  
Fuzhong Weng
Keyword(s):  
Brightness Temperature ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Numerical Weather ◽  
Temperature Anomalies ◽  
Atmospheric Sounding ◽  
Microwave Imager

The main reflector of the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite emits variable radiation, and the SSMIS warm calibration load is intruded by direct and indirect solar radiation. These contamination sources produce antenna brightness temperature anomalies of around 2 K at SSMIS sounding channels which are obviously inappropriate for assimilation into numerical weather prediction models and remote sensing retrievals of atmospheric and surface parameters. In this study, antenna brightness temperature anomalies at several lower atmospheric sounding (LAS) channels are assessed, and the algorithm is developed for corrections of these antenna temperature anomalies. When compared against radiative transfer model simulations and simultaneous observations from AMSU-A aboard NOAA-16, the SSMIS antenna temperatures at 52.8, 53.6, 54.4, 55.5, 57.3, and 59.4 GHz after the anomaly correction exhibit small residual errors (<0.5 K). After such SSMIS antenna temperatures are applied to the National Center for Environmental Prediction Numerical Weather Prediction (NWP) model, more satellite data is used and the analysis field of the geopotential height is significantly improved throughout troposphere and lower stratosphere. Therefore, the SSMIS antenna temperatures after the anomaly correction have demonstrated their potentials in NWP models.

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