4th workshop on assimilating satellite cloud and precipitation observations for NWP

The ability of variational data assimilation to deal with moderate non-linearity and non-Gaussianity is thought to have underpinned recent success in assimilating cloud and precipitation-affected satellite observations using the all-sky approach. A pure ensemble assimilation framework relies on linear and Gaussian assumptions, so its ability to handle all-sky observations is less clear. This...

The present study introduced a non-Gaussian Probability Distribution Function (PDF) and a new displace correction method for precipitation into the dual scale neighboring Ensemble-based Variational assimilation (EnVar) scheme in order to assimilate all-sky Microwave Imager (MWI) brightness temperatures (TBs) into a Cloud-Resolving Model (CRM).

The present study chose the precipitation...

Frozen hydrometeors in tropical cyclones are nonspherical. Moreover, depending on the ambient temperature and ice-supersaturation [1], the vertical inhomogeneity of ice particle habit could exist. Significant efforts have been devoted to studying the nonsphericity effect in microwave radiative transfer [e.g., 2]. However, the vertical inhomogeneity effect has not been addressed so far. In this...

Assimilating all-sky Himawari-8 radiances in the heavy rainfall event on 23 August 2018 in Taiwan

Takumi Honda, Shu-Chih Yang, and Takemasa Miyoshi

In August 2018, a tropical depression stayed near Taiwan and induced heavy precipitation over the southwest region of Taiwan. Detailed evolution of this depression was well captured by the Himawari-8 geostationary satellite of the Japan...

Ensemble data assimilation experiments were used to assess the ability of satellite all-sky infrared brightness temperatures and a nonlinear bias correction (BC) method to improve the accuracy of short-range model forecasts. Infrared brightness temperatures from the SEVIRI sensor that are sensitive to clouds and water vapor in the upper troposphere were assimilated at hourly intervals during a...

Enabled by the new fast and accurate forward operator MFASIS (Scheck et. al, 2016), we work on assimilating solar reflectances measured by the SEVIRI instrument on Meteosat Second Generation in our new convective-scale NWP system ICON-D2-KENDA. We discuss key challenges calling for progress in research areas such as cloud microphysics, data assimilation algorithms to deal with the...

The Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) observed the Earth in lower latitudes between 1997 - 2015. Its conical-scan radiometer has nine channels and measured microwave irradiance between 10 and 89 GHz. These data provide information on atmospheric temperature, humidity, clouds, precipitation, as well as sea surface temperature. Radiance data from other...

Microwave observations are becoming more and more useful for numerical weather prediction ; in particular in an all-sky context which is under development at Météo-France. Indeed those observations can bring highly relevant information content on the vertical distribution of water vapor and hydrometeors. The method under investigation at Meteo-France is called the '1D-Bay+4D-Var' and...

The operational FV3-GFS hybrid data assimilation system assimilates microwave radiances, including those affected by non-precipitating clouds. The cloudy scenes are assumed to be overcast. In recent years efforts have been made to improve the observation operator (CRTM), quality control procedures, and the analysis to make the assimilation of precipitation-affected radiance feasible. ...

Solar photovoltaic power plants and grid operators rely on highly accurate and reliable short-term predictions of solar power and the related prognostic variables such as the surface solar irradiance. These on the other hand strongly depend on the proper forecasting of cloudiness and cloud evolution. Cloud scenarios are best observed by satellites. The use of satellite information in...

Cloud Process Nonlinearity and Model Uncertainty in Data Assimilation and Remote Sensing

Derek J. Posselt and Masashi Minamide

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA

Assimilation of remote sensing observations of clouds and precipitation is challenging for many reasons, including:

• Nonlinearity in cloud and precipitation processes, and in...

The next generation of European polar-orbiting weather satellites (EPS-SG), due to be launched in the 2020s, will carry the novel Ice Cloud Imager (ICI) which has 13 channels measuring frequencies between 183 and 664GHz that are sensitive to scattering by ice crystals in clouds. As well as providing global estimates of bulk ice mass, these observations also have the potential to be assimilated...

Active microwave sensors are becoming widely used observations within the Numerical Weather Prediction community, either for validating model forecasts or for assimilation purposes. Like for the forward simulation of passive microwave observations, radar data simulations require to make assumptions on the scattering properties of hydrometeors. With the objective of simulating both active and...

During the past five years, the Penn State Center for Advanced Data Assimilation and Predictability Techniques (ADAPT) has been devoted to the development and application of techniques to assimilate all-sky radiance at both infrared (IR) and microwave (MW) bands. For radiance from IR imagers, we have developed the Adaptive Observation Error Inflation (AOEI) error model that is able to provide...

This study aims to improve the precipitation forecasts from numerical weather prediction models through effective assimilation of satellite-observed precipitation data. The assimilation of precipitation data is known to be difficult mainly due to highly non-Gaussian statistics of precipitation-related variables. We have been developing a global atmospheric data assimilation system NICAM-LETKF,...

MFASIS is a novel fast radiative transfer method for the simulation of visible satellite images that is fast enough to cope with the computational constraints of operational data assimilation systems and has therefore recently been implemented into RTTOV v12.2 and v12.3. First evaluation and data assimilation experiments using MFASIS in combination with regional models have demonstrated its...

High spatial and temporal resolution image data of the Earth's environment reflecting cloud, precipitating hydrometeor, aerosol, and lower boundary surface conditions abound. Yet the bulk of the information in such observations isn’t yet exploited for the initialization of Numerical Weather Prediction (NWP) model forecasts. This is because most information contained in the imagery data is...

Cloud and precipitation forecasting is both an essential and challenging task in Numerical Weather Prediction (NWP). In this process, a significant part of the errors can be traced back to imperfect initialization of the models. As regards the 3D fields of rain, cloud water, ice crystals, rain and graupel (hydrometeor content fields), several barriers make their initialization a sensitive...

Precipitation and visibility forecasts are crucial for the US Navy; the assimilation of satellite observations in close proximity to the where relevant weather is occurring or will occur is of great value for tactical guidance and decision aids. Assimilation of all-sky microwave imager and sounder data for both temperature and moisture in cloudy areas is an increasingly important source of...

Cloud and precipitation processes are generally highly nonlinear, resulting in strongly non-Gaussian PDF. Particle filters treat non-Gaussian PDF explicitly and would be potentially effective for data assimilation of cloud and precipitation variables. Penny and Miyoshi (2015) developed a Local Particle Filter (LPF) in a form as the ensemble transform matrix of the Local Ensemble Transform...

Motivated by the use of the GFDL microphysics scheme in the FV3GFS, the all-sky radiance assimilation framework has been expanded to include precipitating hydrometeors. In this upgraded all-sky framework, the five hydrometeors, including cloud liquid water, cloud ice, rain, snow and graupel, are the new control variables, replacing the original cloud water control variable. Radiance...

The GNSS Polarimetric Radio Occultations (GNSS PRO) is a new measurement concept being proved aboard the PAZ satellite, operating since May 2018. The technique is based on the 'traditional' GNSS Radio Occultations (GNSS RO), widely used for atmospheric profiling of thermodynamic parameters and assimilated in operational NWP. Adding polarimetric capabilities to the RO system enables to sense...

At present, there are four kinds of sources of ATOVS AMSU-A data available in CMA, including two kinds of DBNet data (as RARS and EUMETcast) and two kinds of global data (as NESDIS and EUMETSAT). There are some differences on satellite kinds, observation coverages and timeliness among different sources.
Up to 2018 only global data are used in global models in CMA. DBNet data have been used...

Total lightning (inter/intra-cloud + cloud-to-ground) is a proven marker of deep convection. Total lightning activity can be documented from low Earth orbit platforms (e. g. the Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission satellite and the International Space Station) and from ground-based lightning locating systems (LLSs). With the advent of optical imagers on...

At DWD the pilot project SINFONY has been set up in 2017 to develop a seamless ensemble prediction system for convective-scale forecasting with forecast ranges of 6 up to 12 hours, which integrates ensemble nowcasting techniques with ensemble numerical model prediction (NWP) in a more or less seamless way. The focus is on severe summertime convective events with associated hazards such as...

The significant positive impact of assimilating AMSU-A in clear sky at ECMWF has long been challenging to replicate when AMSU-A is instead treated in the all-sky data stream. Through various technical changes in the treatment of all-sky AMSU-A data, it is nearing the point where all-sky AMSU-A can outperform its clear-sky counterpart. In hopes of achieving this, the impacts of changes to data...

We present some highlights from recent updates to the all-sky assimilation at ECMWF.

• Changes to consider interchannel correlation in the error model of microwave imagers. For the all-sky assimilation it has been found that a fully-specified covariance matrix that adapts with the cloud amount was needed for this purpose. We find that the tuning of the eigenvectors and the interplay with...