The Use of Composite GOES-R Satellite Imagery to Evaluate a TC Intensity and Vortex Structure Forecast by an FV3GFS-Based Hurricane Forecast Model

The HAFS model is an effort under the NGGPS and UFS initiatives to create the next generation of hurricane prediction and analysis system based on FV3-GFS. It has been validated extensively using traditional verification indicators such as tracker error and biases, intensity error and biases, and the radii of gale, damaging and hurricane strength winds. While satellite images have been used to verify hurricane model forecasts, they have not been used on HAFS. The community radiative transfer model CRTM is used to generate model synthetic satellite images from HAFS model forecast state variables. The 24 forecast snapshots in the mature stage of hurricane Dorian in 2019 are used to generate a composite model synthetic GOES-R infrared brightness image. The composite synthetic image is compared to the corresponding composite image generated from the observed GOES-R data, to evaluate the model forecast TC vortex intensity, size, and asymmetric structure. Results show that the HAFS forecast TC Dorian agrees reasonably well with the observation, but the forecast intensity is weaker, its overall vortex size smaller, and the radii of its eye and maximum winds larger than the observed. The evaluation results can be used to further improve the model. While these results are consistent with those obtained by traditional verification methods, evaluations based on composite satellite images provide an additional benefit with richer information because they have near-real-times spatially and temporally continuous high-resolution data with global coverage. Composite satellite infrared images could be used routinely to supplement traditional verification methods in the HAFS and other hurricane model evaluations. Note since this study only evaluated one hurricane, the above conclusions are only applicable to the model behavior of the mature stage of hurricane Dorian in 2019, and caution is needed to extend these conclusions to expect model biases in predicting other TCs. Nevertheless, the consistency between the evaluation using composite satellite images and the traditional metrics, of hurricane Dorian, shows that this method has the potential to be applied to other storms in future studies.

Improving surface representation and consequences in NWP forecast

<p>The fields that describe surface properties, from terrain height to vegetation types can have substantial impact on NWP model forecast, especially on the model variables close to the surface. These fields can be computed from different databases. Higher resolution of the terrain height database and higher quality of input data leads to a better representation of the terrain height and other surface fields, especially as NWP models move to a higher resolution. Here we use ALARO configuration of the ALADIN System with TOUCANS turbulence scheme (prognostic TKE) with nonhydrostatic dynamics in 2km resolution over Croatia. The model domain contains Dinaric Alps mountains and Adriatic sea.  The existing operational NWP application uses fields from an old database that is insufficient to properly describe the surface in 2km grid spacing. The fields describing topography, such as terrain height, land sea mask, subgrid terrain variability including surface roughness are computed from a new database in substantially higher resolution. The new fields describing the surface characteristics are more realistic, but also substantially different from the fields used before.  However, the model, including the turbulence parametrisation, was tuned using the old database. Therefore, the subsequent model forecast was not automatically improved when the fields from the new database were used. Tuning only one parameter in a scheme is substantial work, but tuning the whole model with a large number of tuning parametres is daunting. Therefore, the computation of surface roughness and other parameters was tuned in order to improve the 10m wind forecast. Decreased surface roughness does not always lead to higher surface wind speeds and vice versa.</p>

Quantifying the Impact of Assimilating Ground-based GNSS in Operational NWP Models

<p>Zenith Total Delay (ZTD) from networks of ground based GNSS receivers has been assimilated in both the Met Office global and high-resolution regional numerical weather predication (NWP) models for over a decade. It is useful to be able to quantify the impact of assimilating these data and compare this with the impact of assimilating other observation types. This helps inform observing network evolution.</p><p>Forecast Sensitivity to Observation Impact (FSOI) analysis is an established method for monitoring the collective impact of an observing network on the quality of an NWP forecast. FSOI uses an adjoint-based method to compute the observation sensitivity for each assimilated observation in a global model forecast simultaneously. The sensitivity value represents the change in forecast error for a unit observation innovation; this information is combined with innovation data from the model forecast to provide an estimate for the resulting change in error for the total 24-hour energy norm. The computational efficiency of the FSOI process makes it a good alternative to traditional Observation System Experiments (OSE) when considering the benefits of an observing network.</p><p>Unfortunately, the underpinning assumptions in the FSOI methodology mean that the method does not easily translate to higher resolution regional NWP models. For these models, observation impacts can still be determined through OSEs through which observations of specific type or types are denied to the model and the results compared with a Control model run with all types of observations included.</p><p>FSOI analysis has previously been used to compare and contrast the benefits associated with different observing method types. This study uses the original FSOI methodology but refines the output to collate impact data at the observing-site level (in relation to a specified observing network).  This enables geographic variations in the impact associated with individual sites within an observing network to be visualised and the tool can be used to assist with decisions on network design and performance.</p><p>Results are presented that illustrate the impact of GNSS ZTD data on global NWP model forecasts on a seasonal and annual basis. A number of metrics are used to assess the benefits associated with a GNSS site, including the total impact over a fixed period of time as well as the mean impact per observation on both a global and country scale. The results highlight the significant impact that individual sites in areas of low data density (such as the southern hemisphere) have on model forecasts.</p><p>The impact of GNSS ZTD data on the 1.5 km resolution UKV model are also presented. The contrasting impacts in summer and winter, and variation of forecast impact as lead-time increases are explored.</p>

Real time weather for autonomous driving and precise road weather forecasts based on floating car data and seamless integration of Ultra-Rapid Data Assimilation and Nowcasting

<p>The growing availability of high resolved meteorological measurements coming from automobiles puts forward the possibility of developing real time weather forecast systems, which appears to be an essential key of autonomous driving enhancement. In this frame, the Fleet Weather Maps (Flotten-Wetter-Karte - FloWKar) project, a joint work of the German Meteorological Service (DWD) and the German car manufacturer AUDI AG, aims to explore how environmental data from sensors of vehicles on Germany’s roads, respecting data protection regulations, can be used in real time to improve weather forecast, nowcasting and warnings within DWD’s products. Regarding weather forecasting, an exceptionally fast data assimilation cycle with an update rate of the order of minutes is necessary. However, this cannot be achieved using standard assimilation systems. Hence, an ultra-rapid data assimilation (URDA) method has been developed. The URDA updates only a reduced version of the state variables in an existing model forecast, using different kind of observation data available, only after a standard assimilation cycle and a full model forecast. Moreover, the quality of the meteorological data collected by moving vehicles is vital and therefore a series of quality control and bias correction algorithms has been built for the correction of the raw observations, employing among others artificial intelligence techniques. The first preliminary results of both project partners are promising: the corrected measured variables of the mass-produced vehicle-based sensors match well with the ‘ground truth’ and real time maps are produced after the assimilation of the high resolved project data. The improved and detailed model outputs for road weather forecasting are a first necessary step towards the safety on roads especially in the winter conditions and the future autonomous driving.</p>

Correction of monthly SST forecast in CFSv2 using the Local Dynamical Analog method

Numerical seasonal forecasts in Earth science always contain forecast errors that cannot be eliminated by improving the ability of the numerical model. Therefore, correction of model forecast results is required. Analog-correction is an effective way to reduce model forecast errors, but the key question is how to locate analogs. In this paper, we updated the Local Dynamical Analog (LDA) algorithm to find analogs and depicted the process of model error correction as the LDA-correction scheme. The LDA-correction scheme was firstly applied to correct the operational seasonal forecasts of sea surface temperature (SST) over the period 1982–2018 from the state-of-the-art coupled climate model named NCEP Climate Forecast System version 2.The results demonstrated that the LDA-correction scheme improves forecast skill in many regions as measured by the correlation coefficient and Root Mean Square Error, especially over the extratropical eastern Pacific and tropical Pacific, where the model has high simulation ability. El Niño-Southern Oscillation (ENSO) as the focused physics process is also improved. The seasonal predictability barrier of ENSO is in remission and the forecast skill of Central Pacific ENSO also increases due to the LDA-correction method. The intensity of ENSO mature phases is improved. Meanwhile, the ensemble forecast results are corrected, which proves the positive influence from this LDA-correction scheme on the probability forecast of cold and warm events. Overall, the LDA-correction scheme, combining statistical and model dynamical information, is demonstrated to be readily integrable with other advanced operational models and has the capability to improve forecast results.

Satellite retrieval of aerosol combined with assimilated forecast

We developed a new aerosol satellite retrieval algorithm combining a numerical aerosol forecast. In the retrieval algorithm, the short-term forecast from an aerosol data assimilation system was used as an a priori estimate instead of spatially and temporally constant values. This method was demonstrated using observation of the Advanced Himawari Imager onboard the Japan Meteorological Agency's geostationary satellite Himawari-8. Overall, the retrieval results incorporated strengths of the observation and the model and complemented their respective weaknesses, showing spatially finer distributions than the model forecast and less noisy distributions than the original algorithm. We validated the new algorithm using ground observation data and found that the aerosol parameters detectable by satellite sensors were retrieved more accurately than an a priori model forecast by adding satellite information. Further, the satellite retrieval accuracy was improved by introducing the model forecast instead of the constant a priori estimates. By using the assimilated forecast for an a priori estimate, information from previous observations can be propagated to future retrievals, leading to better retrieval accuracy. Observational information from the satellite and aerosol transport by the model are incorporated cyclically to effectively estimate the optimum field of aerosol.