scholarly journals The Impact of GPS RO Data on the Prediction of Tropical Cyclogenesis Using a Nonlocal Observation Operator: An Initial Assessment

2020 ◽  
Vol 148 (7) ◽  
pp. 2701-2717 ◽  
Author(s):  
Shu-Ya Chen ◽  
Ying-Hwa Kuo ◽  
Ching-Yuang Huang
Keyword(s):  
Data Assimilation ◽  
North Pacific Ocean ◽  
Probability Of Detection ◽  
Superior Performance ◽  
Tropical Cyclogenesis ◽  
Nonlocal Operator ◽  
Observation Operator ◽  
Local Observation ◽  
Excess Phase ◽  
The Impact

Abstract In this study, the impact of global positioning system (GPS) radio occultation (RO) data on the prediction of the genesis of 10 tropical cyclones over the western North Pacific Ocean is assessed. With the use of a nonlocal excess phase observation operator in cycling data assimilation, the probability of detection for tropical cyclogenesis is increased from 30% to 70% for the cases considered, all of which developed into typhoons. However, the probability of detection is only increased to 40% when a local observation operator is used, indicating that the observation operator can significantly influence the performance of RO data assimilation in capturing tropical cyclogenesis. A nonlocal excess phase operator, which considers the atmospheric horizontal gradients by integrating the refractivity along a ray path, gives superior performance over the local observation operator. Additional sensitivity experiments on 3 of the 10 typhoon cases show that the RO data in the vicinity of the incipient cyclones (within 500 km of the cyclone center) are most critical to successful cyclogenesis prediction. This reflects the fact that having good RO observations at the right time and place is critical for RO to have beneficial impacts on tropical cyclogenesis. Further analyses for Typhoon Nuri (2008) show that assimilation of RO data using the nonlocal operator leads to moistening of the lower and middle troposphere, organized convection, robust grid-scale vertical motions, and the development of midlevel relative vorticity, all of which are favorable for tropical cyclogenesis.

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.

scholarly journals Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

2017 ◽  
Vol 14 ◽  
pp. 187-194 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Claudio Transerici ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Time Range ◽  
False Alarms ◽  
Forecast Time ◽  
The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

A Neural Network-Based Observation Operator for Coupled Ocean-Acoustic Variational Data Assimilation

2021 ◽  
Author(s):  
Andrea Storto ◽  
Giovanni De Magistris ◽  
Silvia Falchetti ◽  
Paolo Oddo
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Automatic Differentiation ◽  
Transmission Loss ◽  
Variational Data Assimilation ◽  
Propagation Model ◽  
Surface Boundary ◽  
Ligurian Sea ◽  
Observation Operator ◽  
The Impact

<p>Variational data assimilation requires implementing the tangent-linear and adjoint (TA/AD) version of any operator. This intrinsically hampers the use of complicated observations. Here, we assess a new data-driven approach to assimilate acoustic underwater propagation measurements (Transmission Loss, TL) into a regional ocean forecasting system. TL measurements depend on the underlying sound speed fields, mostly temperature, and their inversion would require heavy coding of the TA/AD of an acoustic underwater propagation model. In this study, the non-linear version of the acoustic model is applied to an ensemble of perturbed oceanic conditions. TL outputs are used to formulate both a statistical linear operator based on Canonical Correlation Analysis (CCA), and a neural network-based (NN) operator. For the latter, two linearization strategies are compared, the best performing one relying on reverse-mode automatic differentiation. The new observation operator is applied in data assimilation experiments over the Ligurian Sea (Mediterranean Sea), using the Observing System Simulation Experiments (OSSE) methodology to assess the impact of TL observations onto oceanic fields. TL observations are extracted from a nature run with perturbed surface boundary conditions and stochastic ocean physics. Sensitivity analysis and forecast experiments show not only the highest accuracy of the NN reconstruction of TL when compared to CCA, but also that its use in the assimilation of TL observations is able to significantly improve the upper ocean forecast skills. The use of the NN observation operator is computationally affordable, and its general formulation appears promising for the adjoint-free assimilation of any remote sensing observing network.</p>

scholarly journals Improvement of RAMS precipitation forecast at the short range through lightning data assimilation

2016 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Water Vapour ◽  
Short Range ◽  
Daily Precipitation ◽  
Hydrological Cycle ◽  
Atmospheric Modeling ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Total Lightning ◽  
The Impact

Abstract. This study shows the application of a total lightning data assimilation technique to the RAMS (Regional Atmospheric Modeling System) forecast. The method, which can be used at high horizontal resolution, helps to initiate convection whenever flashes are observed by adding water vapour to the model grid column. The water vapour is added as a function of the flash rate, local temperature and graupel mixing ratio. The methodology is set-up to improve the short-term (3 h) precipitation forecast and can be used in real-time forecasting applications. However, results are also presented for the daily precipitation for comparison with other studies. The methodology is applied to twenty cases occurred in fall 2012, that were characterized by widespread convection and lightning activity. For these cases a detailed dataset of hourly precipitation containing thousands of raingauges over Italy, which is the target of this study, is available through the HyMeX (HYdrological cycle in the Mediterranean Experiment) initiative. This dataset gives the unique opportunity to verify the precipitation forecast at the short range (3 h) and over a wide area (Italy). Results for the 27 October case study show how the methodology works and its positive impact on the 3 h precipitation forecast. In particular, the model represents better the convection over the sea using the lightning data assimilation and, when convection is advected over the land, the precipitation forecast improves over the land. It is also shown that the precise location of the convection by lightning data assimilation, improves the precipitation forecast at fine scales (meso-β). The application of the methodology to twenty cases gives a statistically robust evaluation of the impact of the total lightning data assimilation on the model performance. Results show an improvement of all statistical scores, with the exception of the Bias. The Probability of Detection (POD) increases by 3–5 % for the 3 h forecast and by more than 5 % for daily precipitation, depending on the precipitation threshold considered. Score differences between simulations with or without data assimilation are significant at 95 % level for most scores and thresholds considered, showing the positive and statistically robust impact of the lightning data assimilation on the precipitation forecast.

scholarly journals Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation

2017 ◽  
Vol 17 (1) ◽  
pp. 61-76 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Water Vapour ◽  
Short Range ◽  
Daily Precipitation ◽  
Atmospheric Modeling ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Target Area ◽  
Total Lightning ◽  
The Impact

Abstract. This study shows the application of a total lightning data assimilation technique to the RAMS (Regional Atmospheric Modeling System) forecast. The method, which can be used at high horizontal resolution, helps to initiate convection whenever flashes are observed by adding water vapour to the model grid column. The water vapour is added as a function of the flash rate, local temperature, and graupel mixing ratio. The methodology is set up to improve the short-term (3 h) precipitation forecast and can be used in real-time forecasting applications. However, results are also presented for the daily precipitation for comparison with other studies. The methodology is applied to 20 cases that occurred in fall 2012, which were characterized by widespread convection and lightning activity. For these cases a detailed dataset of hourly precipitation containing thousands of rain gauges over Italy, which is the target area of this study, is available through the HyMeX (HYdrological cycle in the Mediterranean Experiment) initiative. This dataset gives the unique opportunity to verify the precipitation forecast at the short range (3 h) and over a wide area (Italy). Results for the 27 October case study show how the methodology works and its positive impact on the 3 h precipitation forecast. In particular, the model represents better convection over the sea using the lightning data assimilation and, when convection is advected over the land, the precipitation forecast improves over the land. It is also shown that the precise location of convection by lightning data assimilation improves the precipitation forecast at fine scales (meso-β). The application of the methodology to 20 cases gives a statistically robust evaluation of the impact of the total lightning data assimilation on the model performance. Results show an improvement of all statistical scores, with the exception of the bias. The probability of detection (POD) increases by 3–5 % for the 3 h forecast and by more than 5 % for daily precipitation, depending on the precipitation threshold considered. Score differences between simulations with or without data assimilation are significant at 95 % level for most scores and thresholds considered, showing the positive and statistically robust impact of the lightning data assimilation on the precipitation forecast.

scholarly journals Application of WRF 3DVAR to Operational Typhoon Prediction in Taiwan: Impact of Outer Loop and Partial Cycling Approaches

2012 ◽  
Vol 27 (5) ◽  
pp. 1249-1263 ◽  
Author(s):  
Ling-Feng Hsiao ◽  
Der-Song Chen ◽  
Ying-Hwa Kuo ◽  
Yong-Run Guo ◽  
Tien-Chiang Yeh ◽  
...  
Keyword(s):  
Data Assimilation ◽  
North Pacific Ocean ◽  
Limited Area ◽  
Track Forecast ◽  
Forecast Errors ◽  
Outer Loop ◽  
Typhoon Track ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract In this paper, the impact of outer loop and partial cycling with the Weather Research and Forecasting Model’s (WRF) three-dimensional variational data assimilation system (3DVAR) is evaluated by analyzing 78 forecasts for three typhoons during 2008 for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings, including Sinlaku, Hagupit, and Jangmi. The use of both the outer loop and the partial cycling approaches in WRF 3DVAR are found to reduce typhoon track forecast errors by more than 30%, averaged over a 72-h period. The improvement due to the outer loop approach, which can be more than 42%, was particularly significant in the early phase of the forecast. The use of the outer loop allows more observations to be assimilated and produces more accurate analyses. The assimilation of additional nonlinear GPS radio occultation (RO) observations over the western North Pacific Ocean, where traditional observational data are lacking, is particularly useful. With the lack of observations over the tropical and subtropical oceans, the error in the first-guess field (which is based on a 6-h forecast of the previous cycle) will continue to grow in a full-cycling limited-area data assimilation system. Even though the use of partial cycling only shows a slight improvement in typhoon track forecast after 12 h, it has the benefit of suppressing the growth of the systematic model error. A typhoon prediction model using the Advanced Research core of the WRF (WRF-ARW) and the WRF 3DVAR system with outer loop and partial cycling substantially improves the typhoon track forecast. This system, known as Typhoon WRF (TWRF), has been in use by CWB since 2010 for operational typhoon predictions.

scholarly journals Comparison between 3D-Var and 4D-Var data assimilation methods for the simulation of a heavy rainfall case in central Italy

2017 ◽  
Vol 14 ◽  
pp. 271-278 ◽  
Author(s):  
Vincenzo Mazzarella ◽  
Ida Maiello ◽  
Vincenzo Capozzi ◽  
Giorgio Budillon ◽  
Rossella Ferretti
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Hydrological Cycle ◽  
Central Italy ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Heavy Rainfall Event ◽  
Tyrrhenian Sea ◽  
Pressure System ◽  
The Impact

Abstract. This work aims to provide a comparison between three dimensional and four dimensional variational data assimilation methods (3D-Var and 4D-Var) for a heavy rainfall case in central Italy. To evaluate the impact of the assimilation of reflectivity and radial velocity acquired from Monte Midia Doppler radar into the Weather Research Forecasting (WRF) model, the quantitative precipitation forecast (QPF) is used.The two methods are compared for a heavy rainfall event that occurred in central Italy on 14 September 2012 during the first Special Observation Period (SOP1) of the HyMeX (HYdrological cycle in Mediterranean EXperiment) campaign. This event, characterized by a deep low pressure system over the Tyrrhenian Sea, produced flash floods over the Marche and Abruzzo regions, where rainfall maxima reached more than 150 mm 24 h−1.To identify the best QPF, nine experiments are performed using 3D-Var and 4D-Var data assimilation techniques. All simulations are compared in terms of rainfall forecast and precipitation measured by the gauges through three statistical indicators: probability of detection (POD), critical success index (CSI) and false alarm ratio (FAR). The assimilation of conventional observations with 4D-Var method improves the QPF compared to 3D-Var. In addition, the use of radar measurements in 4D-Var simulations enhances the performances of statistical scores for higher rainfall thresholds.

scholarly journals Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

2021 ◽  
Vol 25 (12) ◽  
pp. 6283-6307
Author(s):  
Sara Modanesi ◽  
Christian Massari ◽  
Alexander Gruber ◽  
Hans Lievens ◽  
Angelica Tarpanelli ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Bayesian Optimization ◽  
Irrigation Scheme ◽  
Area Index ◽  
Observation Operator ◽  
Future Data ◽  
The Impact

Abstract. Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic crop rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. This work represents the first and necessary step towards building a reliable LSM data assimilation system which, in future analysis, will investigate the potential of high-resolution radar backscatter observations from Sentinel-1 to improve irrigation quantification. Specifically, the aim of this study is to couple the Noah-MP LSM running within the NASA Land Information System (LIS), with a backscatter observation operator for simulating unbiased backscatter predictions over irrigated lands. In this context, we first tested how well modelled surface soil moisture (SSM) and vegetation estimates, with or without irrigation simulation, are able to capture the signal of aggregated 1 km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.

scholarly journals The impact of a new high-resolution ocean model on the Met Office North-West European Shelf forecasting system

Ocean Science ◽  
2019 ◽  
Vol 15 (4) ◽  
pp. 1133-1158 ◽  
Author(s):  
Marina Tonani ◽  
Peter Sykes ◽  
Robert R. King ◽  
Niall McConnell ◽  
Anne-Christine Péquignet ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Superior Performance ◽  
North West ◽  
Resolution System ◽  
European Shelf ◽  
Forecasting System ◽  
The Impact ◽  
New System ◽  
West European

Abstract. The North-West European Shelf ocean forecasting system has been providing oceanographic products for the European continental shelf seas for more than 15 years. In that time, several different configurations have been implemented, updating the model and the data assimilation components. The latest configuration to be put in operation, an eddy-resolving model at 1.5 km (AMM15), replaces the 7 km model (AMM7) that has been used for 8 years to deliver forecast products to the Copernicus Marine Environment Monitoring Service and its precursor projects. This has improved the ability to resolve the mesoscale variability in this area. An overview of this new system and its initial validation is provided in this paper, highlighting the differences with the previous version. Validation of the model with data assimilation is based on the results of 2 years (2016–2017) of trial experiments run with the low- and high-resolution systems in their operational configuration. The 1.5 km system has been validated against observations and the low-resolution system, trying to understand the impact of the high resolution on the quality of the products delivered to the users. Although the number of observations is a limiting factor, especially for the assessment of model variables like currents and salinity, the new system has been proven to be an improvement in resolving fine-scale structures and variability and provides more accurate information on the major physical variables, like temperature, salinity, and horizontal currents. AMM15 improvements are evident from the validation against high-resolution observations, available in some selected areas of the model domain. However, validation at the basin scale and using daily means penalized the high-resolution system and does not reflect its superior performance. This increment in resolution also improves the capabilities to provide marine information closer to the coast even if the coastal processes are not fully resolved by the model.

A Neural Network-Based Observation Operator for Coupled Ocean-Acoustic Variational Data Assimilation

2021 ◽  
Author(s):  
Andrea Storto ◽  
Giovanni De Magistris ◽  
Silvia Falchetti ◽  
Paolo Oddo
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Propagation Model ◽  
Sensitivity Analyses ◽  
Surface Boundary ◽  
Ligurian Sea ◽  
Observation Operator ◽  
Skill Scores ◽  
The Impact

AbstractVariational data assimilation requires implementing the tangent-linear and adjoint (TA/AD) version of any operator. This intrinsically hampers the use of complicated observations. Here, we assess a new data-driven approach to assimilate acoustic underwater propagation measurements (Transmission Loss, TL) into a regional ocean forecasting system. TL measurements depend on the underlying sound speed fields, mostly temperature, and their inversion would require heavy coding of the TA/AD of an acoustic underwater propagation model. In this study, the non-linear version of the acoustic model is applied to an ensemble of perturbed oceanic conditions. TL outputs are used to formulate both a statistical linear operator based on canonical correlation analysis (CCA), and a neural network-based (NN) operator. For the latter, two linearization strategies are compared, the best-performing one relying on reverse-mode automatic differentiation. The new observation operator is applied in data assimilation experiments over the Ligurian Sea (Mediterranean Sea), using the Observing System Simulation Experiments (OSSE) methodology to assess the impact of TL observations onto oceanic fields. TL observations are extracted from a nature run with perturbed surface boundary conditions and stochastic ocean physics. Sensitivity analyses indicate that the NN reconstruction of TL is significantly better than CCA. Both CCA and NN are able to improve the upper ocean skill scores in forecast experiments, with NN outperforming CCA on the average. The use of the NN observation operator is computationally affordable, and its general formulation appears promising for the adjoint-free assimilation of any remote sensing observing network.

Sign in / Sign up

Export Citation Format

Share Document