scholarly journals The use of GNSS zenith total delays in operational AROME/Hungary 3D-Var over a Central European domain

2018 ◽  
Author(s):  
Máté Mile ◽  
Patrik Benáček ◽  
Szabolcs Rózsa
Keyword(s):  
Data Assimilation ◽  
Bias Correction ◽  
Weather Prediction ◽  
Time Estimation ◽  
Satellite System ◽  
Added Value ◽  
Limited Area ◽  
Central European ◽  
The Impact ◽  
Assimilation Scheme

Abstract. The delay of satellite signals broadcasted by Global Navigation Satellite System (GNSS) provides unique atmospheric observation which endorses numerial weather prediction from global to limited-area models. Due to the possibility of its frequent and near real-time estimation, the zenith total delays (ZTD) are valuable information for any state-of-the-art data assimilation systems. This article introduces the data assimilation of ZTDs in a Hungarian numerical weather prediction system which was carried out taking into account observations from Central-European GNSS analysis and processing centres. The importance of ZTD observations is described and showed by a diagnostic tool in the three hourly updated 3D-Var variational assimilation scheme. Furthermore, observing system experiments are done to evaluate the impact of GNSS ZTDs on mesoscale limited-area forecasts. The results of the use of GNSS ZTDs showed a clear added value to improve screen-level temperature and humidity forecasts when bias is accurately estimated and corrected in the data assimilation scheme. The importance of variational i.e. adaptive bias correction is highlighted by verification scores compared to static bias correction. Moreover, this paper reviews the quality control of GNSS ground-based stations inside the Central-European domain, the calculation of optimal thinning distance and the preparation of two above mentioned bias correction methods. At the end of this article, the conclusion is drawn about different settings of the forecast and analysis experiments with a brief future outlook.

scholarly journals The use of GNSS zenith total delays in operational AROME/Hungary 3D-Var over a central European domain

2019 ◽  
Vol 12 (3) ◽  
pp. 1569-1579 ◽  
Author(s):  
Máté Mile ◽  
Patrik Benáček ◽  
Szabolcs Rózsa
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Bias Correction ◽  
Weather Prediction ◽  
Added Value ◽  
Limited Area ◽  
Central European ◽  
Numerical Weather ◽  
The Impact ◽  
Assimilation Scheme

Abstract. The delay of satellite signals broadcasted by Global Navigation Satellite System (GNSS) provides unique atmospheric observations which endorse numerical weather prediction from global to limited-area models. Due to the possibility of its frequent and near-real-time estimation, the zenith total delays (ZTDs) are valuable information for any state-of-the-art data assimilation system. This article introduces the data assimilation of ZTDs in a Hungarian numerical weather prediction system, which was carried out by taking into account observations from central European GNSS analysis and processing centres. The importance of ZTD observations is described and shown by a diagnostic tool in the 3-hourly updated 3D-Var assimilation scheme. Furthermore, observing system experiments are done to evaluate the impact of GNSS ZTDs on mesoscale limited-area forecasts. The results of the use of GNSS ZTDs showed a clear added value to improve screen-level temperature and humidity forecasts when the bias is accurately estimated and corrected in the data assimilation scheme. The importance of variational, i.e. adaptive bias correction, is highlighted by verification scores compared to static bias correction. Moreover, this paper reviews the quality control of GNSS ground-based stations inside the central European domain, the calculation of optimal thinning distance and the preparation of the two above-mentioned bias correction methods. Finally, conclusions are drawn on different settings of the forecast and analysis experiments with a brief future outlook.

scholarly journals Data assimilation of GNSS zenith total delays from a Nordic processing centre

2017 ◽  
Vol 17 (22) ◽  
pp. 13983-13998 ◽  
Author(s):  
Magnus Lindskog ◽  
Martin Ridal ◽  
Sigurdur Thorsteinsson ◽  
Tong Ning
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Bias Correction ◽  
Weather Prediction ◽  
Satellite System ◽  
Initial State ◽  
Numerical Weather ◽  
Processing Centre ◽  
The Impact ◽  
Forecast Quality

Abstract. Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

scholarly journals Data assimilation of GNSS Zenith Total Delays from a Nordic processing centre

2017 ◽  
Author(s):  
Magnus Lindskog ◽  
Martin Ridal ◽  
Sigurdur Thorsteinsson ◽  
Tong Ning
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Bias Correction ◽  
Weather Prediction ◽  
Satellite System ◽  
Initial State ◽  
Numerical Weather ◽  
Processing Centre ◽  
The Impact ◽  
Forecast Quality

Abstract. Atmospheric moisture-related information obtained from Global Navigation Satellite System (GNSS) observations from ground-based receiver stations of the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art km-scale numerical weather prediction system. Different processing techniques have been implemented to derive the the moisture-related GNSS information in the form of Zenith Total Delays (ZTD) and these are described and compared. In addition full scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the following forecast quality. The sensitivity of results to aspects of the data processing, observation density, bias-correction and data assimilation have been investigated. Results show a benefit on forecast quality of using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

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.

Estimation of the Added Value of the Absolute Calibration of GPS Radio Occultation Data for Numerical Weather Prediction

2015 ◽  
Vol 143 (4) ◽  
pp. 1259-1274 ◽  
Author(s):  
Josep M. Aparicio ◽  
Stéphane Laroche
Keyword(s):  
Bias Correction ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Added Value ◽  
Atmospheric Measurements ◽  
Gps Radio Occultation ◽  
Correction Scheme ◽  
Occultation Data ◽  
The Impact ◽  
Radio Occultation Data

Abstract An analysis of the impact of GPS radio occultation observations on Environment Canada’s global deterministic weather prediction system is presented. Radio occultation data, as any other source of weather observations, have a direct impact on the analyses. Since they are assimilated assuming that they are well calibrated, they also impact the bias correction scheme employed for other data, such as satellite radiances. The authors estimate the relative impact of occultation data obtained from, first, their assimilation as atmospheric measurements and, second, their influence on the bias correction for radiance data. This assessment is performed using several implementations of the thermodynamic relationships involved, and also allowing or blocking this influence to the radiance bias correction scheme. The current implementation of occultation operators at Environment Canada is presented, collecting upgrades that have been detailed elsewhere, such as the equation of state of air and the expression of refractivity. The performance of the system with and without assimilation of occultations is reviewed under conditions representative of current operations. Several denial runs are prepared, withdrawing only the occultation data from the assimilation, but keeping their influence on the radiance bias correction, or assimilating occultations but denying their impact on the bias correction procedure, and a complete denial. It is shown that the impact of occultations on the analysis is significant through both paths—assimilation and radiance bias correction—albeit the first is larger. The authors conclude that the traceability link of the ensemble of occultations has an added value, beyond the value of each datum as an atmospheric measurement.

scholarly journals Impact of Assimilating China Precipitation Analysis Data Merging with Remote Sensing Products Using the 4DVar Method on the Prediction of Heavy Rainfall

2019 ◽  
Vol 11 (8) ◽  
pp. 973 ◽  
Author(s):  
Yuanbing Wang ◽  
Yaodeng Chen ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Eastern China ◽  
Wrf Model ◽  
Analysis Data ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Precipitation Analysis ◽  
The Impact

In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications.

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 Variational Bias Correction of GNSS ZTD in the HARMONIE Modeling System

2016 ◽  
Vol 55 (5) ◽  
pp. 1259-1276 ◽  
Author(s):  
Jana Sánchez Arriola ◽  
Magnus Lindskog ◽  
Sigurdur Thorsteinsson ◽  
Jelena Bojarova
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Case Studies ◽  
Bias Correction ◽  
Processing System ◽  
Individual Case ◽  
Scale Model ◽  
Observation System ◽  
Limited Area ◽  
The Impact

AbstractTo fill the gap in the observation system for humidity, the HIRLAM–ALADIN Research on Mesoscale Operational NWP in Euromed (HARMONIE) limited-area high-resolution kilometer-scale model has been prepared for assimilation of Global Navigation Satellite System (GNSS) zenith total delay (ZTD) observations. The observation-processing system includes data selection, bias correction, quality control, and a GNSS observation operator for data assimilation. A large part of the bias between observations and model equivalents comes from the relatively low model top used in the HARMONIE experiments. The functionality of the different observation-processing components was investigated in detail as was the overall performance of the GNSS ZTD data assimilation. This paper contains an extensive description of the GNSS ZTD observation-processing system and a comparison of a newly introduced variational bias correction for GNSS ZTD data with an alternative static bias correction, as well as a detailed analysis of the impact of GNSS ZTD data, both in terms of statistical evaluations over a longer period and in terms of individual case studies. Assimilation of the GNSS ZTD observations with a variational bias correction has improved the quality of short-range weather forecasts for the moisture-related parameters in particular, both in a statistical sense and in individual case studies. The paper also discusses further improvements in the HARMONIE variational data-assimilation system that are needed to fully utilize the potential of high-resolution GNSS ZTD observations.

scholarly journals Evaluating the use of Aeolus satellite observations in the regional NWP model Harmonie-Arome

2021 ◽  
Author(s):  
Susanna Hagelin ◽  
Roohollah Azad ◽  
Magnus Lindskog ◽  
Harald Schyberg ◽  
Heiner Körnich
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Line Of Sight ◽  
Limited Area ◽  
Horizontal Line ◽  
Observation Error ◽  
Potential Improvement ◽  
Nwp Model ◽  
The Impact

Abstract. The impact of using wind speed data 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 Sept–Oct 2018 when the satellite was recently launched, and a later period in Apr–May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations have 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 generate flow-dependent analysis increments, with the Aeolus data.

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.

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