An Update on the Impact of Aeolus Doppler Wind Lidar Observations for Use in Numerical Weather Prediction at ECMWF

2021 ◽  
Author(s):  
Michael P. Rennie ◽  
Lars Isaksen
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Lower Stratosphere ◽  
Positive Impact ◽  
Weather Prediction ◽  
Horizontal Line ◽  
Numerical Weather ◽  
Wind Retrievals ◽  
Atmospheric Backscatter ◽  
The Impact

<p>The latest results on the assessment of the impact of Aeolus Level-2B horizontal line-of-sight wind retrievals in global Numerical Weather Prediction at ECMWF will be presented.  Aeolus has been operationally assimilated at ECMWF since 9 January 2020.<br>Random and systematic error estimates were derived from observation minus background departure statistics.  The HLOS wind random error standard deviation is estimated to vary over the range 4.0-7.0 m/s for the Rayleigh-clear and 2.8-3.6 m/s for the Mie-cloudy; depending on atmospheric signal levels which in turn depends on instrument performance, atmospheric backscatter properties and the processing algorithms.<br>In Observing System Experiments (OSEs) Aeolus provides statistically significant improvement in short-range forecasts as verified by observations sensitive to temperature, wind and humidity.  Longer forecast range verification shows positive impact that is strongest at the 2-3 day forecast range; ~2% improvement in root mean square error for vector wind and temperature in the tropical upper troposphere and lower stratosphere and polar troposphere.  Positive impact up to 9 days is found in the tropical lower stratosphere.  Both Rayleigh-clear and Mie-cloudy winds provide positive impact, but the Rayleigh accounts for most tropical impact. The Forecast Sensitivity Observation Impact (FSOI) metric is available since Aeolus was operationally assimilated, which confirms Aeolus is a useful contribution to the global observing system; with the Rayleigh-clear and Mie-cloudy winds providing similar overall short-range impact in 2020.  If the OSEs are ready in time, we will present the impact of the first reprocessed Aeolus data for the July-December 2019 period.</p>

An Assessment of the Impact of Aeolus Doppler Wind Lidar Observations for Use in Numerical Weather Prediction at ECMWF

2020 ◽  
Author(s):  
Michael P. Rennie ◽  
Lars Isaksen
Keyword(s):  
Numerical Weather Prediction ◽  
Positive Impact ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Forecast Errors ◽  
Horizontal Line ◽  
Numerical Weather ◽  
Medium Range ◽  
Ecmwf Model ◽  
The Impact

<p>The European Space Agency’s Aeolus mission, which was launched in August 2018, provides profiles of horizontal line-of-sight (HLOS) wind observations from a polar orbiting satellite.  The European Centre For Medium-Range Weather Forecasts (ECMWF) began the operational assimilation of Aeolus Level-2B winds on 9 January 2020 in their global NWP (Numerical Weather Prediction) model, 1 year and 4 months after the first Level-2B wind products were produced in near real time via ESA’s ground processing segment.  This achievement was possible because of the production of good data quality, which was met through a close collaboration of all the parties involved within the Aeolus Data Innovation and Science Cluster (DISC) and via the great efforts of ESA, industry and ground processing algorithms pre- and post-launch.<br>Through the careful assessment of the statistics of differences of the Aeolus winds relative to the ECMWF model, the Level-2B Rayleigh winds were found to have large systematic errors.  The systematic errors were found to be highly correlated with ALADIN’s (Atmospheric Laser Doppler Instrument) primary mirror temperatures, which vary in a complex manner due to the variation in Earthshine and thermal control of the mirror.  The correction of this source of bias in the ground processing is underway, therefore in the meantime a bias correction scheme using the ECMWF model as a reference was developed for successful data assimilation; the scheme will be described.  <br>We will present the results of the Aeolus NWP impact assessment which led to the decision to go operational.  Aeolus’ second laser (FM-B, available since late June 2019) provides statistically significant positive impact of moderate to large amplitude, of similar magnitude to some other important and well-established observing systems (such as IR radiances, GNNS radio occultation and Atmospheric Motion Vectors).  Observing System Experiments demonstrate reduction of forecast errors in geopotential and vector wind of around 2% in the tropics and 2-3% in the southern hemisphere for short-range and medium range forecasts (up to day 10).  This positive impact is particularly impressive given that Aeolus provides less than 1% of the total number of observations assimilated, showing the value of direct wind observations for global NWP.</p>

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 4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

2018 ◽  
Author(s):  
Witold Rohm ◽  
Jakub Guzikowski ◽  
Karina Wilgan ◽  
Maciej Kryza
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Positive Impact ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Severe Weather ◽  
Numerical Weather ◽  
Gnss Data ◽  
The Impact

Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications in meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models. With this approach, the best estimate of current conditions consistent with both information sources is produced. Some approaches allow assimilating also the non-prognostic variables, including remote sensing data from radar or GNSS (Global Navigation Satellite System). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. This paper shows the results of assimilation of GNSS data into numerical weather prediction (NWP) model WRF (Weather Research and Forecasting). The WRF model offers two different variational approaches: 3DVAR and 4DVAR, both available through WRF Data Assimilation (WRFDA) package. The WRFDA assimilation procedure was modified to correct for bias and observation errors. We assimilated the Zenith Troposphere Delay (ZTD), Precipitable Water (PW), radiosonde (RS) and surface synoptic observations (SYNOP) using 4DVAR assimilation scheme. Three experiments have been performed: (1) assimilation of PW and ZTD for May and June of 2013, (2) assimilation of: PW alone; PW, with RS and SYNOP; ZTD alone; and finally ZTD, with RS and SYNOP for 5–23 May 2013, and (3) assimilation of PW or ZTD during severe weather events in June 2013. Once the initial conditions were established, the forecast was run for 48 hours. The obtained WRF predictions are validated against surface meteorological measurements, including air temperature, humidity, wind speed, and rainfall rate. Results from the first experiment (May and June 2013) show that the assimilation of GNSS data (both ZTD and PW) have positive impact on the rain and humidity forecast. However, the assimilation of ZTD is more successful, and brings substantial reduction of errors in rain forecast by 8 %, and a 20 % improvement in bias of humidity forecast, but it has a slight negative impact on temperature bias and wind speed. Second experiment (5–23 May 2013) reveals that the PW or ZTD assimilation leads to a similar reduction of errors as in the first experiment, moreover, adding SYNOP and RS observations to the assimilation does not improve the humidity or rain forecasts (in the 48 h forecast) but reduces errors in the wind speed and temperature. Furthermore, short term predictions (up to 24 h) of rain and humidity are better when SYNOP and RS observations are assimilated. The impact of assimilation of ZTD and PW in severe weather cases is mixed, one out of three investigated cases shows positive impact of GNSS data, whereas other two neutral or negative.

scholarly journals Homogeneity criteria from AVHRR information within IASI pixels in a numerical weather prediction context

2019 ◽  
Vol 12 (6) ◽  
pp. 3001-3017
Author(s):  
Imane Farouk ◽  
Nadia Fourrié ◽  
Vincent Guidard
Keyword(s):  
Numerical Weather Prediction ◽  
Mesoscale Model ◽  
Positive Impact ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Detection Scheme ◽  
Numerical Weather ◽  
Clear Sky ◽  
The Impact

Abstract. This article focuses on the selection of satellite infrared IASI (Infrared Atmospheric Sounding Interferometer) observations in the global numerical weather prediction (NWP) system ARPEGE (Action de Recherche Petite Echelle Grande Echelle). The observation simulation is performed with the sophisticated radiative transfer model RTTOV-CLD, which takes into account the cloud scattering and the multilayer clouds from atmospheric profiles and cloud microphysical profiles (liquid water content, ice content and cloud fraction). The aim of this work is to select homogeneous scenes by using the information of the collocated Advanced Very High Resolution Radiometer (AVHRR) pixels inside each IASI field of view and to retain the most favourable cases for the assimilation of IASI infrared radiances. Two methods to select homogeneous scenes using homogeneity criteria already proposed in the literature were adapted: the criteria derived from Martinet et al. (2013) for cloudy sky selection in the French mesoscale model AROME (Applications of Research to Operations at MEsoscale) and the criteria from Eresmaa (2014) for clear-sky selection in the global model IFS (Integrated Forecasting System). A comparison between these methods reveals considerable differences, in both the method to compute the criteria and the statistical results. From this comparison a revised method representing a kind of compromise between the different tested methods is proposed and it uses the two infrared AVHRR channels to define the homogeneity criteria in the brightness temperature space. This revised method has a positive impact on the observation minus the simulation statistics, while retaining 36 % of observations for the assimilation. It was then tested in the NWP system ARPEGE for the clear-sky assimilation. These criteria were added to the current data selection based on the McNally and Watts (2003) cloud detection scheme. It appears that the impact on analyses and forecasts is rather neutral.

scholarly journals On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

2018 ◽  
Vol 146 (2) ◽  
pp. 599-622 ◽  
Author(s):  
David D. Flagg ◽  
James D. Doyle ◽  
Teddy R. Holt ◽  
Daniel P. Tyndall ◽  
Clark M. Amerault ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Research Laboratory ◽  
Weather Prediction ◽  
Ocean Model ◽  
Naval Research ◽  
Unmanned Aerial System ◽  
Numerical Weather ◽  
The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

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 Existence of multiple scales in uncertainty of numerical weather prediction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hyo-Jong Song
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Multiple Scales ◽  
Forecast Error ◽  
Weather Prediction ◽  
Initial Condition ◽  
Forecast Uncertainty ◽  
Essential Information ◽  
Numerical Weather ◽  
Sample Correlation

Abstract Numerical weather prediction provides essential information of societal influence. Advances in the initial condition estimation have led to the improvement of the prediction skill. The process to produce the better initial condition (analysis) with the combination of short-range forecast and observation over the globe requires information about uncertainty of the forecast results to decide how much observation is reflected to the analysis and how far the observation information should be propagated. Forecast ensemble represents the error of the short-range forecast at the instance. The influence of observation propagating along with forecast ensemble correlation needs to be restricted by localized correlation function because of less reliability of sample correlation. So far, solitary radius of influence is usually used since there has not been an understanding about the realism of multiple scales in the forecast uncertainty. In this study, it is explicitly shown that multiple scales exist in short-range forecast error and any single-scale localization approach could not resolve this situation. A combination of Gaussian correlation functions of various scales is designed, which more weighs observation itself near the data point and makes ensemble perturbation, far from the observation position, more participate in decision of the analysis. Its outstanding performance supports the existence of multi-scale correlation in forecast uncertainty.

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