Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part II: Combined Radar and Satellite Data Experiments

Abstract This research represents the second part of a two-part series describing the development of a prototype ensemble data assimilation system for the Warn-on-Forecast (WoF) project known as the NSSL Experimental WoF System for ensembles (NEWS-e). Part I describes the NEWS-e design and results from radar reflectivity and radial velocity data assimilation for six severe weather events occurring during 2013 and 2014. Part II describes the impact of assimilating satellite liquid and ice water path (LWP and IWP, respectively) retrievals from the GOES Imager along with the radar observations. Assimilating LWP and IWP observations may improve thermodynamic conditions at the surface over the storm-scale domain through better analysis of cloud coverage in the model compared to radar-only experiments. These improvements sometimes corresponded to an improved analysis of supercell storms leading to better forecasts of low-level vorticity. This positive impact was most evident for events where convection is not ongoing at the beginning of the radar and satellite data assimilation period. For more complex cases containing significant amounts of ongoing convection, only assimilating clear-sky satellite retrievals in place of clear-air reflectivity resulted in spurious regions of light precipitation compared to the radar-only experiments. The analyzed tornadic storms in these experiments are sometimes too weak and quickly diminished in intensity in the forecasts. The lessons learned as part of these experiments should lead to improved iterations of the NEWS-e system, building on the modestly successful results described here.

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Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

Remote Sensing ◽

10.3390/rs11202364 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2364 ◽

Cited By ~ 4

Author(s):

Małgorzata Werner ◽

Maciej Kryza ◽

Jakub Guzikowski

Keyword(s):

Air Pollution ◽

Data Assimilation ◽

Eastern Europe ◽

Satellite Data ◽

Positive Impact ◽

Large Degree ◽

Significant Difference ◽

The Impact ◽

Air Pollution Episodes ◽

Pollution Episodes

Based on the Weather Research and Forecasting model with Chemistry (WRF-Chem) model and Gridpoint Statistical Interpolation (GSI) assimilation tool, a forecasting system was used for two selected episodes (winter and summer) over Eastern Europe. During the winter episode, very high particular matter (PM2.5, diameter less than 2.5 µm) concentrations, related to low air temperatures and increased emission from residential heating, were measured at many stations in Poland. During the summer episode, elevated aerosol optical depth (AOD), likely related to the transport of pollution from biomass fires, was observed in Southern Poland. Our aim is to verify if there is a relevant positive impact of surface and satellite data assimilation (DA) on modeled PM2.5 concentrations, and to assess whether there are significant differences in the DA’s impact on concentrations between the two seasons. The results show a significant difference in the impact of surface and satellite DA on the model results between the summer and winter episode, which to a large degree is related to the availability of the satellite data. For example, the application of satellite DA raises the factor of two statistic from 0.18 to 0.78 for the summer episode, whereas this statistic remains unchanged (0.71) for the winter. The study suggests that severe winter air pollution episodes in Poland and Eastern Europe in general, often related to the dense cover of low clouds, will benefit from the assimilation of surface observations rather than satellite data, which can be very sparse in such meteorological situations. In contrast, the assimilation of satellite data can have a greater positive impact on the model results during summer than the assimilation of surface data for the same period.

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Variational Assimilation of Cloud Liquid/Ice Water Path and Its Impact on NWP

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-14-0243.1 ◽

2015 ◽

Vol 54 (8) ◽

pp. 1809-1825 ◽

Cited By ~ 18

Author(s):

Yaodeng Chen ◽

Hongli Wang ◽

Jinzhong Min ◽

Xiang-Yu Huang ◽

Patrick Minnis ◽

...

Keyword(s):

Data Assimilation ◽

Liquid Water ◽

Weather Prediction ◽

Wrf Model ◽

Liquid Water Path ◽

Cloud Liquid Water ◽

Water Path ◽

Ice Water Path ◽

Ice Water ◽

The Impact

AbstractAnalysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

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SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

10.20944/preprints201904.0216.v1 ◽

2019 ◽

Author(s):

Nemesio Rodriguez-Fernandez ◽

Patricia de Rosnay ◽

Clement Albergel ◽

Philippe Richaume ◽

Filipe Aires ◽

...

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Positive Impact ◽

Land Surface Model ◽

Surface Model ◽

Northern Asia ◽

The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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Data assimilation of SMOS observations into the Mercator Ocean operational system: focus on the Nino 2015 event

10.5194/os-2018-113 ◽

2018 ◽

Cited By ~ 5

Author(s):

Benoît Tranchant ◽

Elisabeth Remy ◽

Eric Greiner ◽

Olivier Legalloudec

Keyword(s):

Data Assimilation ◽

Ocean Circulation ◽

Positive Impact ◽

Atmospheric Precipitation ◽

Sea Surface ◽

Sea Surface Salinity ◽

Global Ocean ◽

Data Sets ◽

The Impact ◽

Over The Top

Abstract. Monitoring Sea Surface Salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the acceleration of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, most often closer to 5 meters deep than the surface, were available to estimate the SSS. The recent satellite missions of ESA's SMOS, NASA's Aquarius, and now SMAP have made possible for the first time to measure SSS from space. The SSS drivers can be quite different than the temperature ones. The model SSS can suffer from significant errors coming not only from the ocean dynamical model but also the atmospheric precipitation and evaporation as well as ice melting and river runoff. Satellite SSS can bring a valuable additional constraint to control the model salinity. In the framework of the SMOS Nino 2015 ESA project (https://www.godae-oceanview.org/projects/smos-nino15/), the impact of satellite SSS data assimilation is assessed with the Met Office and Mercator Ocean global ocean analysis and forecasting systems with a focus on the Tropical Pacific region. This article presents the analysis of an Observing System Experiment (OSE) conducted with the 1/4° resolution Mercator Ocean analysis and forecasting system. SSS data assimilation constrains the model SSS to be closer to the observations in a coherent way with the other data sets already routinely assimilated in an operational context. Globally, the SMOS SSS assimilation has a positive impact in salinity over the top 30 meters. Comparisons to independent data sets show a small but positive impact. The sea surface height (SSH) has also been impacted by implying a reinforcement of TIWs during the El-Niño 2015/16 event. Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS performance.

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Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

Nonlinear Processes in Geophysics ◽

10.5194/npg-21-1027-2014 ◽

2014 ◽

Vol 21 (5) ◽

pp. 1027-1041 ◽

Cited By ~ 6

Author(s):

K. Apodaca ◽

M. Zupanski ◽

M. DeMaria ◽

J. A. Knaff ◽

L. D. Grasso

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Mesoscale Model ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

New Information ◽

Forecasting System ◽

Maximum Likelihood Ensemble Filter ◽

The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

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Improving air quality forecasting with the assimilation of GOCI AOD retrievals

10.5194/egusphere-egu2020-2804 ◽

2020 ◽

Author(s):

Soyoung Ha ◽

Zhiquan Liu

Keyword(s):

Air Quality ◽

Data Assimilation ◽

South Korea ◽

Positive Impact ◽

Fine Particulate Matter ◽

Three Dimensional ◽

Basis Set ◽

Upstream Region ◽

The Impact ◽

Air Quality Forecasting

<p>The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula. In this study, the GOCI Aerosol optical depth (AOD), retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Over the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. The talk will be finished with an introduction of our ongoing efforts on developing the assimilation capability for more sophisticated aerosol schemes such as Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Modal Aerosol Dynamics Model for Europe (MADE)-Volatility basis set (VBS).</p>

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

Monthly Weather Review ◽

10.1175/mwr-d-20-0174.1 ◽

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.

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Comparison of Evaporation and Cold Pool Development between Single-Moment and Multimoment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms

Monthly Weather Review ◽

10.1175/2009mwr2956.1 ◽

2010 ◽

Vol 138 (4) ◽

pp. 1152-1171 ◽

Cited By ~ 139

Author(s):

Daniel T. Dawson ◽

Ming Xue ◽

Jason A. Milbrandt ◽

M. K. Yau

Keyword(s):

Flank Region ◽

Real Data ◽

Cold Pool ◽

Cold Bias ◽

Data Simulation ◽

Supercell Storms ◽

Thermodynamic Conditions ◽

Single Moment ◽

Size Sorting ◽

The Impact

Abstract Idealized simulations of the 3 May 1999 Oklahoma tornadic supercell storms are conducted at various horizontal grid spacings ranging from 1 km to 250 m, using a sounding extracted from a prior 3-km grid spacing real-data simulation. A sophisticated multimoment bulk microphysics parameterization scheme capable of predicting up to three moments of the particle or drop size distribution (DSD) for several liquid and ice hydrometeor species is evaluated and compared with traditional single-moment schemes. The emphasis is placed on the impact of microphysics, specifically rain evaporation and size sorting, on cold pool strength and structure, and on the overall reflectivity structure of the simulated storms. It is shown through microphysics budget analyses and examination of specific processes within the low-level downdraft regions that the multimoment scheme has important advantages, which lead to a weaker and smaller cold pool and better reflectivity structure, particularly in the forward-flank region of the simulated supercells. Specifically, the improved treatment of evaporation and size sorting, and their effects on the predicted rain DSDs by the multimoment scheme helps to control the cold bias often found in the simulations using typical single-moment schemes. The multimoment results are more consistent with observed (from both fixed and mobile mesonet platforms) thermodynamic conditions within the cold pools of the discrete supercells of the 3 May 1999 outbreak.

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Enhancements of Satellite Data Assimilation over Antarctica

Monthly Weather Review ◽

10.1175/2009mwr3071.1 ◽

2010 ◽

Vol 138 (6) ◽

pp. 2149-2173 ◽

Cited By ~ 18

Author(s):

Aurélie Bouchard ◽

Florence Rabier ◽

Vincent Guidard ◽

Fatima Karbou

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Satellite Data ◽

Additional Data ◽

Positive Impact ◽

High Latitudes ◽

Surface Emissivity ◽

Atmospheric Infrared Sounder ◽

Microwave Sounding ◽

Snow Precipitation

Abstract The Concordiasi field experiment, which is taking place in Antarctica, involves the launching of radiosoundings and stratospheric balloons. One of the main goals of this campaign is the validation of the Infrared Atmospheric Sounding Interferometer (IASI) radiance assimilation. Prior to the campaign, it was necessary to improve satellite data assimilation at high latitudes. Two types of sensors, microwave and infrared, have been considered to help with this issue. A major problem associated with microwave satellite data is the calculation of the surface emissivity. An innovative approach, based on satellite observations, improves the surface emissivity modeling over land and sea ice within the constraints of the four-dimensional variational data assimilation (4D-VAR) system. With this new calculation of emissivity, it has been possible to include many more microwave observations during the assimilation. In this study, this method has been applied to high latitudes, after some adjustments have been made to assimilate additional Advanced Microwave Sounding Unit-A/B (AMSU-A/B) data over sea ice and snow. The use of additional data from IASI and the Atmospheric Infrared Sounder (AIRS) sensors over land and sea ice has also been tested. The use of the microwave and infrared data over this polar area has modified the dynamical and thermodynamical model fields such as the snow precipitation quantity. Additional data have been found to have a positive impact on the skill of a model specially tuned for Antarctica.

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Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model

Nonlinear Processes in Geophysics Discussions ◽

10.5194/npgd-1-917-2014 ◽

2014 ◽

Vol 1 (1) ◽

pp. 917-952

Author(s):

K. Apodaca ◽

M. Zupanski ◽

M. DeMaria ◽

J. A. Knaff ◽

L. D. Grasso

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Mesoscale Model ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

New Information ◽

Forecasting System ◽

Maximum Likelihood Ensemble Filter ◽

The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Goestationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), improving initial conditions, and partially improving WRF-NMM forecasts during several data assimilation cycles.

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