W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors

Abstract. The development of ground-based cloud radars offers a new capability to continuously monitor fog structure. Retrievals of fog microphysics are key for future process studies, data assimilation, or model evaluation and can be performed using a variational method. Both the one-dimensional variational retrieval method (1D-Var) or direct 3D/4D-Var data assimilation techniques rely on the combination of cloud radar measurements and a background profile weighted by their corresponding uncertainties to obtain the optimal solution for the atmospheric state. In order to prepare for the use of ground-based cloud radar measurements for future applications based on variational approaches, the different sources of uncertainty due to instrumental, background, and forward operator errors need to be properly treated and accounted for. This paper aims at preparing 1D-Var retrievals by analysing the errors associated with a background profile and a forward operator during fog conditions. For this, the background was provided by a high-resolution numerical weather prediction model and the forward operator by a radar simulator. Firstly, an instrumental dataset was taken from the SIRTA observatory near Paris, France, for winter 2018–2019 during which 31 fog events were observed. Statistics were calculated comparing cloud radar observations to those simulated. It was found that the accuracy of simulations could be drastically improved by correcting for significant spatio-temporal background errors. This was achieved by implementing a most resembling profile method in which an optimal model background profile is selected from a domain and time window around the observation location and time. After selecting the background profiles with the best agreement with the observations, the standard deviation of innovations (observations–simulations) was found to decrease significantly. Moreover, innovation statistics were found to satisfy the conditions needed for future 1D-Var retrievals (un-biased and normally distributed).

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W-band Radar Observations for Fog Forecast Improvement: an Analysis of Model and Forward Operator Errors

10.5194/amt-2020-468 ◽

2021 ◽

Author(s):

Alistair Bell ◽

Pauline Martinet ◽

Olivier Caumont ◽

Benoît Vié ◽

Julien Delanoë ◽

...

Keyword(s):

Data Assimilation ◽

Time Window ◽

Weather Prediction ◽

Optimal Solution ◽

Numerical Weather Prediction Model ◽

Radar Observations ◽

Cloud Radar ◽

Fog Microphysics ◽

Radar Measurements ◽

Forward Operator

Abstract. The development of ground based cloud radars offers a new capability to continuously monitor the fog structure. Retrievals of fog microphysics is key for future process studies, data assimilation or model evaluation, and can be performed using a variational method. Both the one-dimensional variational retrieval method (1D-Var) or direct 3D/4D-Var data assimilation techniques rely on the combination of cloud radar measurements and a background profile weighted by their corresponding uncertainties to obtain the optimal solution for the atmospheric state. In order to prepare for the use of ground-based cloud radar measurements for future applications based on variational approaches, the different sources of uncertainty due to instrumental, background, and the forward operator errors need to be properly treated and accounted for. This paper aims at preparing 1D-Var retrievals by analysing the errors associated with a background profile and a forward operator during fog conditions. For this, the background was provided by a high-resolution numerical weather prediction model and the forward operator by a radar simulator. Firstly, an instrumental dataset was taken from the SIRTA observatory near Paris, France for winter 2018–19 during which 31 fog events were observed. Statistics were calculated comparing cloud radar observations to those simulated. It was found that the accuracy of simulations could be drastically improved by correcting for significant spatio-temporal background errors. This was achieved by implementing a most resembling profile method in which an optimal model background profile is selected from a domain and time window around the observation location and time. After selecting the best background profile a good agreement was found between observations and simulations. Moreover, observation minus simulation errors were found to satisfy the conditions needed for future 1D-var retrievals (un-biased and normally distributed).

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Benefit of microwave radiometer and cloud radar observations for data assimilation and fog process studies during the SOFOG3D experiment

10.5194/ems2021-232 ◽

2021 ◽

Author(s):

Pauline Martinet ◽

Frédéric Burnet ◽

Alistair Bell ◽

Arthur Kremer ◽

Matthias Letillois ◽

...

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Weather Prediction ◽

Ensemble Prediction ◽

Initial State ◽

Ensemble Prediction System ◽

Radar Observations ◽

Stratus Clouds ◽

Cloud Radar ◽

Physical Parameterizations

<p>Fog forecasts still remain quite inaccurate due to the complexity, non linearities and fine scale of the main physical processes driving the fog lifecycle. Additionally to the complex modelling of fog processes, current numerical weather prediction models are known to suffer from a lack of operational observations in the atmospheric boundary layer and more generally during cloudy-sky conditions. Continuous observations of both thermodynamics and microphysics during the fog lifecycle are thus essential to develop future operational networks with the aim of validating current physical parameterizations and improving the model initial state through data assimilation techniques. In this context, an international network of 8 ground-based microwave radiometers (MWRs) has been deployed at a regional-scale on a 300 x 300 km domain during the SOFOG3D (SOuth FOGs 3D experiment for fog processes study) that has been conducted from October 2019 to April 2020. The MWR network has been extended with ceilometers at all MWR sites and additional microphysical observations from the 95 GHz cloud radar BASTA at two major sites as well as wind measurements from a Doppler lidar deployed at the super-site. After an overview of the SOFOG3D objectives and experimental set-up, preliminary results exploiting mainly the MWR network and cloud radar observations will be presented. Firstly, the capability of MWRs to provide temperature and humidity retrievals within fog and stratus clouds will be evaluated and discussed against radiosoundings launched during intensive observation periods (IOPs). Secondly, first retrievals of liquid water content profiles within fog and stratus clouds derived from the synergy between MWRs and the BASTA cloud radar will be presented. To that end, a one dimensional variational approach (1D-Var) directly assimilating MWR brightness temperatures and cloud-radar reflectivities has been developed. 1D-Var retrievals will be validated through a dataset of simulated observations and real fog cases of the SOFOG3D experiment. The capability of MWR and cloud radar observations to improve the initial state of the AROME model during fog conditions will be discussed with a focus on selected case studies. Finally, the usefulness of ground-based remote sensing networks to improve our understanding of fog processes and to validate physical parameterizations will be illustrated using the operational AROME model and the AROME Ensemble Prediction System</p>

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Driftsonde Observations to Evaluate Numerical Weather Prediction of the Late 2006 African Monsoon

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-0176.1 ◽

2013 ◽

Vol 52 (4) ◽

pp. 974-995 ◽

Cited By ~ 4

Author(s):

Philippe Drobinski ◽

Fatima Karbou ◽

Peter Bauer ◽

Philippe Cocquerez ◽

Christophe Lavaysse ◽

...

Keyword(s):

Data Assimilation ◽

Numerical Weather Prediction ◽

Vertical Structure ◽

Weather Prediction ◽

Meridional Wind ◽

Numerical Weather Prediction Model ◽

African Monsoon ◽

Easterly Waves ◽

Numerical Weather ◽

Multidisciplinary Analysis

AbstractDuring the international African Monsoon Multidisciplinary Analysis (AMMA) project, stratospheric balloons carrying gondolas called driftsondes capable of dropping meteorological sondes were deployed over West Africa and the tropical Atlantic Ocean. The goals of the deployment were to test the technology and to study the African easterly waves, which are often the forerunners of hurricanes. Between 29 August and 22 September 2006, 124 sondes were dropped over the seven easterly waves that moved across Africa into the Atlantic between about 10° and 20°N, where almost no in situ vertical information exists. Conditions included waves that developed into Tropical Storm Florence and Hurricanes Gordon and Helene. In this study, a selection of numerical weather prediction model outputs has been compared with the dropsondes to assess the effect of some developments in data assimilation on the quality of analyses and forecasts. By comparing two different versions of the Action de Recherche Petite Echelle Grande Echelle (ARPEGE) model of Météo-France with the dropsondes, first the benefits of the last data assimilation updates are quantified. Then comparisons are carried out using the ARPEGE model and the Integrated Forecast System (IFS) model of the European Centre for Medium-Range Weather Forecasts. It is shown that the two models represent very well the vertical structure of temperature and humidity over both land and sea, and particularly within the Saharan air layer, which displays humidity below 5%–10%. Conversely, the models are less able to represent the vertical structure of the meridional wind. This problem seems to be common to ARPEGE and IFS, and its understanding still requires further investigations.

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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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Application of a Lightning Data Assimilation Technique in the WRF-ARW Model at Cloud-Resolving Scales for the Tornado Outbreak of 24 May 2011

Monthly Weather Review ◽

10.1175/mwr-d-11-00299.1 ◽

2012 ◽

Vol 140 (8) ◽

pp. 2609-2627 ◽

Cited By ~ 72

Author(s):

Alexandre O. Fierro ◽

Edward R. Mansell ◽

Conrad L. Ziegler ◽

Donald R. MacGorman

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Three Dimensional ◽

Numerical Weather Prediction Model ◽

Analysis Time ◽

Test Case ◽

Forecast Errors ◽

Mixing Ratio ◽

Total Lightning ◽

Tornado Outbreak

Abstract This study presents the assimilation of total lightning data to help initiate convection at cloud-resolving scales within a numerical weather prediction model. The test case is the 24 May 2011 Oklahoma tornado outbreak, which was characterized by an exceptional synoptic/mesoscale setup for the development of long-lived supercells with large destructive tornadoes. In an attempt to reproduce the observed storms at a predetermined analysis time, total lightning data were assimilated into the Weather Research and Forecasting Model (WRF) and analyzed via a suite of simple numerical experiments. Lightning data assimilation forced deep, moist precipitating convection to occur in the model at roughly the locations and intensities of the observed storms as depicted by observations from the National Severe Storms Laboratory’s three-dimensional National Mosaic and Multisensor Quantitative Precipitation Estimation (QPE)—i.e., NMQ—radar reflectivity mosaic product. The nudging function for the total lightning data locally increases the water vapor mixing ratio (and hence relative humidity) via a simple smooth continuous function using gridded pseudo-Geostationary Lightning Mapper (GLM) resolution (9 km) flash rate and simulated graupel mixing ratio as input variables. The assimilation of the total lightning data for only a few hours prior to the analysis time significantly improved the representation of the convection at analysis time and at the 1-h forecast within the convective permitting and convective resolving grids (i.e., 3 and 1 km, respectively). The results also highlighted possible forecast errors resulting from errors in the initial mesoscale thermodynamic variable fields. Although this case was primarily an analysis rather than a forecast, this simple and computationally inexpensive assimilation technique showed promising results and could be useful when applied to events characterized by moderate to intense lightning activity.

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Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

10.20944/preprints201808.0068.v1 ◽

2018 ◽

Author(s):

Serguei Ivanov ◽

Silas Michaelides ◽

Igor Ruban

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Radar Data ◽

Numerical Weather Prediction Model ◽

Precipitation Prediction ◽

Cell Patterns ◽

Reflectivity Data ◽

Rain Rates ◽

Pattern Presentation ◽

Radar Data Assimilation

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5–3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

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Improved Prediction of Landfalling Tropical Cyclone in China Based on Assimilation of Radar Radial Winds with New Super-Observation Processing

Weather and Forecasting ◽

10.1175/waf-d-20-0002.1 ◽

2020 ◽

Vol 35 (6) ◽

pp. 2523-2539

Author(s):

Jianing Feng ◽

Yihong Duan ◽

Qilin Wan ◽

Hao Hu ◽

Zhaoxia Pu

Keyword(s):

Data Assimilation ◽

Inner Core ◽

Doppler Radar ◽

Weather Prediction ◽

Extreme Rainfall ◽

Radar Data ◽

Numerical Weather Prediction Model ◽

Previous Method ◽

Horizontal Wind ◽

The Impact

AbstractThis work explores the impact of assimilating radial winds from the Chinese coastal Doppler radar on track, intensity, and quantitative precipitation forecasts (QPF) of landfalling tropical cyclones (TCs) in a numerical weather prediction model, focusing mainly on two aspects: 1) developing a new coastal radar super-observation (SO) processing method, namely, an evenly spaced thinning method (ESTM) that is fit for landfalling TCs, and 2) evaluating the performance of the radar radial wind data assimilation in QPFs of landfalling TCs with multiple TC cases. Compared to a previous method of generating SOs (i.e., the radially spaced thinning method), in which the density of SOs is equal within the radial space of a radar scanning volume, the SOs created by ESTM are almost evenly distributed in the horizontal grids of the model background, resulting in more observations located in the TC inner-core region being involved in SOs. The use of SOs from ESTM leads to more cyclonic wind innovation, and larger analysis increments of height and horizontal wind in the lower level in an ensemble Kalman filter data assimilation experiment with TC Mujigae (2015). Overall, forecasts of a TC’s landfalling position, intensity, and QPF are improved by radar data assimilation for all cases, including Mujigae and the other eight TCs that made landfall on the Chinese mainland in 2017. Specifically, through assimilation, TC landing position error and intensity error are reduced by 33% and 25%, respectively. The mean equitable threat score of extreme rainfall [>80 mm (3 h)−1] forecasts is doubled on average over all cases.

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Predicting Typhoon Morakot’s Catastrophic Rainfall with a Convection-Permitting Mesoscale Ensemble System

Weather and Forecasting ◽

10.1175/2010waf2222414.1 ◽

2010 ◽

Vol 25 (6) ◽

pp. 1816-1825 ◽

Cited By ~ 42

Author(s):

Fuqing Zhang ◽

Yonghui Weng ◽

Ying-Hwa Kuo ◽

Jeffery S. Whitaker ◽

Baoguo Xie

Keyword(s):

Data Assimilation ◽

Real Time ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Decision Makers ◽

Extreme Weather Events ◽

Management Decision ◽

Typhoon Morakot ◽

Weather Events ◽

Flooding Event

Abstract This study examines the prediction and predictability of the recent catastrophic rainfall and flooding event over Taiwan induced by Typhoon Morakot (2009) with a state-of-the-art numerical weather prediction model. A high-resolution convection-permitting mesoscale ensemble, initialized with analysis and flow-dependent perturbations obtained from a real-time global ensemble data assimilation system, is found to be able to predict this record-breaking rainfall event, producing probability forecasts potentially valuable to the emergency management decision makers and the general public. Since all the advanced modeling and data assimilation techniques used here are readily available for real-time operational implementation provided sufficient computing resources are made available, this study demonstrates the potential and need of using ensemble-based analysis and forecasting, along with enhanced computing, in predicting extreme weather events like Typhoon Morakot at operational centers.

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On the extraction of wind information from the assimilation of ozone profiles in Météo–France 4-D-Var operational NWP suite

Atmospheric Chemistry and Physics ◽

10.5194/acp-9-4855-2009 ◽

2009 ◽

Vol 9 (14) ◽

pp. 4855-4867 ◽

Cited By ~ 39

Author(s):

N. Semane ◽

V.-H. Peuch ◽

S. Pradier ◽

G. Desroziers ◽

L. El Amraoui ◽

...

Keyword(s):

Data Assimilation ◽

Degrees Of Freedom ◽

Variance Reduction ◽

Lower Stratosphere ◽

Transport Model ◽

Weather Prediction ◽

Error Variance ◽

Numerical Weather Prediction Model ◽

Background Error ◽

Better Than

Abstract. By applying four-dimensional variational data-assimilation (4-D-Var) to a combined ozone and dynamics Numerical Weather Prediction model (NWP), ozone observations generate wind increments through the ozone-dynamics coupling. The dynamical impact of Aura/MLS satellite ozone profiles is investigated using Météo-France operational ARPEGE NWP 4-D-Var assimilation system for a period of 3 months. A data-assimilation procedure has been designed and run on 6-h windows. The procedure includes: (1) 4-D-Var assimilating both ozone and operational NWP standard observations, (2) ARPEGE transporting ozone as a passive-tracer, (3) MOCAGE, the Météo–France chemistry and transport model re-initializing the ARPEGE ozone background at the beginning time of the assimilation window. Using observation minus forecast statistics, it is found that the ozone assimilation reduces the wind bias in the lower stratosphere. Moreover, the Degrees of Freedom for Signal diagnostics show that the MLS data covering the 68.1–31.6 hPa vertical pressure range are the most informative and their information content is nearly of the same order as tropospheric humidity-sensitive radiances. Furthermore, with the help of error variance reduction diagnostics, the ozone contribution to the reduction of the horizontal divergence background-error variance is shown to be better than tropospheric humidity-sensitive radiances.

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Time-Expanded Sampling for Ensemble-Based Data Assimilation Applied to Conventional and Satellite Observations

Weather and Forecasting ◽

10.1175/waf-d-14-00108.1 ◽

2015 ◽

Vol 30 (4) ◽

pp. 855-872 ◽

Cited By ~ 4

Author(s):

Qingyun Zhao ◽

Qin Xu ◽

Yi Jin ◽

Justin McLay ◽

Carolyn Reynolds

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Naval Research ◽

Ensemble Size ◽

Nested Grids ◽

Effectiveness And Efficiency ◽

Time Critical ◽

Computational Resources ◽

Operational Constraints

Abstract The time-expanded sampling (TES) method, designed to improve the effectiveness and efficiency of ensemble-based data assimilation and subsequent forecast with reduced ensemble size, is tested with conventional and satellite data for operational applications constrained by computational resources. The test uses the recently developed ensemble Kalman filter (EnKF) at the Naval Research Laboratory (NRL) for mesoscale data assimilation with the U.S. Navy’s mesoscale numerical weather prediction model. Experiments are performed for a period of 6 days with a continuous update cycle of 12 h. Results from the experiments show remarkable improvements in both the ensemble analyses and forecasts with TES compared to those without. The improvements in the EnKF analyses by TES are very similar across the model’s three nested grids of 45-, 15-, and 5-km grid spacing, respectively. This study demonstrates the usefulness of the TES method for ensemble-based data assimilation when the ensemble size cannot be sufficiently large because of operational constraints in situations where a time-critical environment assessment is needed or the computational resources are limited.

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