scholarly journals Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator

2019 ◽  
Vol 12 (9) ◽  
pp. 4829-4848 ◽  
Author(s):  
Natalia Hanna ◽  
Estera Trzcina ◽  
Gregor Möller ◽  
Witold Rohm ◽  
Robert Weber
Keyword(s):  
Standard Deviation ◽  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Forecasting ◽  
Heavy Precipitation ◽  
Global Navigation Satellite Systems ◽  
Weather Research And Forecasting ◽  
The Impact ◽  
Precipitation Events ◽  
Weather Research

Abstract. From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3-D grid of voxels, and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting, the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of central Europe during the period of 29 May–14 June 2013, when heavy-precipitation events were observed. For both models, slant wet delays (SWDs) were calculated based on estimates of zenith total delay (ZTD) and horizontal gradients, provided for 88 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model), in order to assess the impact of different factors on the tomographic solution. The GNSS tomography outputs have been assimilated into the nested (12 and 36 km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3D-Var) system, in particular, its radio occultation observation operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy-precipitation events. Future improvements to the assimilation method are also discussed.

scholarly journals Assimilation of GNSS tomography products into WRF using radio occultation data assimilation operator

2019 ◽  
Author(s):  
Natalia Hanna ◽  
Estera Trzcina ◽  
Gregor Möller ◽  
Witold Rohm ◽  
Robert Weber
Keyword(s):  
Standard Deviation ◽  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Forecasting ◽  
Heavy Precipitation ◽  
Horizontal Resolution ◽  
Global Navigation Satellite Systems ◽  
Total Delay ◽  
Water Vapour Content ◽  
Precipitation Events

Abstract. From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a novel technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3D grid of voxels and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting – the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of Central Europe during the period of 29 May–14 June 2013, when heavy precipitation events were observed. For both models, Slant Wet Delays (SWD) were calculated based on estimates of Zenith Total Delay (ZTD) and horizontal gradients, provided for 72 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model). The GNSS tomography outputs have been assimilated into the nested (12- and 36-km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3DVar) system, in particular its radio occultation observations operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy precipitation events. Future improvements to the assimilation method are also discussed.

Parallelization Strategies for the GPS Radio Occultation Data Assimilation with a Nonlocal Operator in the Weather Research and Forecasting Model

2014 ◽  
Vol 31 (9) ◽  
pp. 2008-2014 ◽  
Author(s):  
Xin Zhang ◽  
Ying-Hwa Kuo ◽  
Shu-Ya Chen ◽  
Xiang-Yu Huang ◽  
Ling-Feng Hsiao
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Observation Operator ◽  
Gps Radio Occultation ◽  
Phase Observation ◽  
Excess Phase ◽  
Weather Research

Abstract The nonlocal excess phase observation operator for assimilating the global positioning system (GPS) radio occultation (RO) sounding data has been proven by some research papers to produce significantly better analyses for numerical weather prediction (NWP) compared to the local refractivity observation operator. However, the high computational cost and the difficulties in parallelization associated with the nonlocal GPS RO operator deter its application in research and operational NWP practices. In this article, two strategies are designed and implemented in the data assimilation system for the Weather Research and Forecasting Model to demonstrate the capability of parallel assimilation of GPS RO profiles with the nonlocal excess phase observation operator. In particular, to solve the parallel load imbalance problem due to the uneven geographic distribution of the GPS RO observations, round-robin scheduling is adopted to distribute GPS RO observations among the processing cores to balance the workload. The wall clock time required to complete a five-iteration minimization on a demonstration Antarctic case with 106 GPS RO observations is reduced from more than 3.5 h with a single processing core to 2.5 min with 106 processing cores. These strategies present the possibility of application of the nonlocal GPS RO excess phase observation operator in operational data assimilation systems with a cutoff time limit.

Improved impacts in observing system simulation experiments of radio occultation observations as a result of model and data assimilation changes

2020 ◽  
Author(s):  
L. CUCURULL ◽  
S. P. F. CASEY
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Forecasting ◽  
Positive Impact ◽  
System Simulation ◽  
Weather Prediction ◽  
Wind Fields ◽  
Simulation Experiments ◽  
The Impact ◽  
Forward Operator

AbstractAs global data assimilation systems continue to evolve, Observing System Simulation Experiments (OSSEs) need to be updated to accurately quantify the impact of proposed observing technologies in weather forecasting. Earlier OSSEs with radio occultation (RO) observations have been updated and the impact of the originally proposed Constellation Observing Satellites for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) mission, with a high-inclination and low-inclination component, has been investigated by using the operational data assimilation system at NOAA and a 1-dimensional bending angle RO forward operator. It is found that the impact of the low-inclination component of the originally planned COSMIC-2 mission (now officially named COSMIC-2) has significantly increased as compared to earlier studies, and significant positive impact is now found globally in terms of mass and wind fields. These are encouraging results as COSMIC-2 was successfully launched in June 2019 and data have been recently released to operational weather centers. Earlier findings remain valid indicating that globally distributed RO observations are more important to improve weather prediction globally than a denser sampling of the tropical latitudes. Overall, the benefits reported here from assimilating RO soundings are much more significant than the impacts found in previous OSSEs. This is largely attributed to changes in the data assimilation and forecast system and less to the more advanced 1-dimensional forward operator chosen for the assimilation of RO observations.

scholarly journals Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

2020 ◽  
Vol 12 (3) ◽  
pp. 383 ◽  
Author(s):  
Christos Giannaros ◽  
Vassiliki Kotroni ◽  
Konstantinos Lagouvardos ◽  
Theodore M. Giannaros ◽  
Christos Pikridas
Keyword(s):  
Data Assimilation ◽  
Time Window ◽  
Positive Impact ◽  
High Impact ◽  
Global Navigation Satellite Systems ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Tropospheric Delay ◽  
The Impact ◽  
Precipitation Events

The derivation of global navigation satellite systems (GNSSs) tropospheric products is nowadays a state-of-the-art technique that serves both research and operational needs in a broad range of applications in meteorology. In particular, GNSS zenith tropospheric delay (ZTD) data assimilation is widely applied in Europe to enhance numerical weather predictions (NWPs). The current study presents the first attempt at introducing assimilation of ZTDs, derived from more than 48 stations of the Hellenic GNSS network, into the operational NWP system of the National Observatory of Athens (NOA) in Greece, which is based on the mesoscale Weather Research and Forecasting (WRF) model. WRF was applied during seven high-impact precipitation events covering the dry and wet season of 2018. The simulation employing the ZTD data assimilation reproduces more accurately, compared to the control experiment, the observed heavy rainfall (especially for high precipitation events, exceeding 20 mm in 24h) during both dry and wet periods. Assimilating ZTDs also improves the simulation of intense (>20 mm) convective precipitation during the time window of its occurrence in the dry season, and provides a beneficial influence during synoptic-scale events in the wet period. The above results, which are statistically significant, highlight an important positive impact of ZTD assimilation on the model’s precipitation forecast skill over Greece. Overall, the modelling system’s configuration, including the assimilation of ZTD observations, satisfactorily captures the spatial and temporal distribution of the observed rainfall and can therefore be used as the basis for examining further improvements in the future.

scholarly journals Moisture Recycling over the Iberian Peninsula: The Impact of 3DVAR Data Assimilation

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 19
Author(s):  
Santos J. González-Rojí ◽  
Jon Sáenz ◽  
Javier Díaz de Argandoña ◽  
Gabriel Ibarra-Berastegi
Keyword(s):  
Data Assimilation ◽  
Iberian Peninsula ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Spatiotemporal Distribution ◽  
Weather Research And Forecasting ◽  
Atmospheric Moisture ◽  
Moisture Recycling ◽  
The Impact ◽  
Weather Research

In this paper, we have estimated the spatiotemporal distribution of moisture recycling over the Iberian Peninsula (IP). The recycling ratio was computed from two simulations over the IP using the Weather Research and Forecasting (WRF) model with a horizontal resolution of 15 km spanning the period 2010–2014. The first simulation (WRF N) was nested inside the ERA-Interim with information passed to the domain through the boundaries. The second run (WRF D) is similar to WRF N, but it also includes 3DVAR data assimilation every six hours (12:00 a.m., 6:00 a.m., 12:00 p.m. and 6:00 p.m. UTC). It was also extended until 2018. The lowest values of moisture recycling (3%) are obtained from November to February, while the highest ones (16%) are observed in spring in both simulations. Moisture recycling is confined to the southeastern corner during winter. During spring and summer, a gradient towards the northeastern corner of the IP is observed in both simulations. The differences between both simulations are associated with the dryness of the soil in the model and are higher during summer and autumn. WRF D presents a lower bias and produces more reliable results because of a better representation of the atmospheric moisture.

TOMOREF operator for assimilation of GNSS tomography wet refractivity fields in WRF DA system

2020 ◽  
Author(s):  
Natalia Hanna ◽  
Estera Trzcina ◽  
Maciej Kryza ◽  
Witold Rohm
Keyword(s):  
Data Assimilation ◽  
Weather Forecasting ◽  
Negative Impact ◽  
Wrf Model ◽  
Weather Conditions ◽  
Heavy Precipitation ◽  
Global Navigation Satellite Systems ◽  
Total Delay ◽  
Observation Operator ◽  
Tomography Data

<p>The amount of water vapor in the atmosphere is highly variable and not easy to measure. One of the methods to provide reliable information about the amount and distribution of the humidity in the troposphere is GNSS (Global Navigation Satellite Systems) tomography. The GNSS tomography uses the observations of signal delays between satellites and ground-based receivers over the field covered by a GNSS network. This method enables deriving the 3D distribution of wet refractivity at a low cost in all weather conditions, with high temporal and spatial resolution.</p><p>The first applications of the GNSS tomography data in the Weather Research and Forecasting Data Assimilation (WRF DA) system were performed by the adaptation of the GPSREF observation operator. In this study, we present a new tool, namely the TOMOREF observation operator, which consists of three parts: forward, tangent linear, and adjoint operators. As the input data in the assimilation process, the wet refractivity fields from two tomographic models (TUW, WUELS) are used. The analysis is carried out for a 2-week long period (May 29 – June 14, 2013) in Central Europe when severe weather conditions occurred, including heavy precipitation events. The data assimilation results are verified against radiosonde observations, synoptic data, and ERA5 reanalysis. Moreover, the performance of the TOMOREF and GPSREF operators is examined. For the forecasts of relative humidity (RH) at a pressure level of 300 hPa, the implementation of the TOMOREF operator vanishes the negative impact caused by the GPSREF operator. Additionally, the improvement of the root mean square error of the forecasts of RH up to 0.5% is observed. Comparing to the assimilation of Zenith Total Delay observations, the application of the tomographic data has overall a greater influence on the WRF model. Consequently, the GNSS tomography data can be valuable in operational weather forecasting.</p>

scholarly journals Optimization of the WRFV3.7 adjoint model

2018 ◽  
Author(s):  
Qiang Cheng ◽  
Juanjuan Liu ◽  
Bin Wang
Keyword(s):  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Time Cost ◽  
Input Output ◽  
Adjoint Model ◽  
New Strategy ◽  
Weather Research

Abstract. This work focused on a new strategy for productively improving the performance of adjoint models. By using several techniques including the push/pop-free method, careful Input/Output (IO) analysis and the use of the conception of adjoint locality, we reduced the adjoint cost of the Weather Research and Forecasting plus (WRFPLUS) by almost half on different numbers of processors especially with a slight decrease in total memory. Several experiments are conducted using the four-dimensional variational data assimilation (4DVar) method. The results show that the total time cost of running a 4DVar application is decreased by approximately 1/3.

Evaluating the Impact of the COSMIC RO Bending Angle Data on Predicting the Heavy Precipitation Episode on 16 June 2008 during SoWMEX-IOP8

2014 ◽  
Vol 142 (11) ◽  
pp. 4139-4163 ◽  
Author(s):  
Shu-Chih Yang ◽  
Shu-Hua Chen ◽  
Shu-Ya Chen ◽  
Ching-Yuang Huang ◽  
Ching-Sen Chen
Keyword(s):  
Lower Troposphere ◽  
Heavy Precipitation ◽  
Southwest Monsoon ◽  
Weather Research And Forecasting ◽  
Heavy Rainfall Event ◽  
Bending Angle ◽  
Angle Data ◽  
Ensemble Transform Kalman Filter ◽  
The Impact ◽  
Better Than

Abstract Global positioning system (GPS) radio occultation (RO) data have been broadly used in global and regional numerical weather predictions. Assimilation with the bending angle often performs better than refractivity, which is inverted from the bending angle under spherical assumption and is sometimes associated with negative biases at the lower troposphere; however, the bending angle operator also requires a higher model top as used in global models. This study furnishes the feasibility of bending-angle assimilation in the prediction of heavy precipitation systems with a regional model. The local RO operators for simulating bending angle and refractivity are implemented in the Weather Research and Forecasting (WRF)–local ensemble transform Kalman filter (LETKF) framework. The impacts of assimilating RO data from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) using both operators are evaluated on the prediction of a heavy precipitation episode during Southwest Monsoon Experiment intensive observing period 8 (SoWMEX-IOP8) in 2008. Results show that both the refractivity and bending angle provide a favorable condition for generating this heavy rainfall event. In comparison with the refractivity data, the advantage of assimilating the bending angle is identified in the midtroposphere for deepening of the moist layer that leads to a rainfall forecast closer to the observations.

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.

Sign in / Sign up

Export Citation Format

Share Document