Ensemble-Based Targeted Observation Method Applied to Radar Radial Velocity Observations on Idealized Supercell Low-Level Rotation Forecasts: A Proof of Concept

The potential future installation of a multifunction phased-array radar (MPAR) network will provide capabilities of case-specific adaptive scanning. Knowing the impacts adaptive scanning may have on short-term forecasts will influence scanning strategy decision-making in hopes to produce the most optimal ensemble forecast while also benefiting human severe weather warning decision-making. An ensemble-based targeted observation algorithm is applied to an observing system simulation experiment (OSSE) where the impacts of synthetic idealized supercell radial velocity observations are estimated before the observations are “collected” and assimilated. The forecast metric of interest is the low-level rotation forecast metric (0–1-km updraft helicity), a surrogate for tornado prediction. It is found that the ensemble-based targeted observation approach can reasonably estimate the true error variance reduction when an effective method that treats sampling error is applied, the period of model forecast is associated with less degrees of nonlinearity, and the observation information content relative to the background forecast is larger. In some scenarios, a subset of a full-volume scan assimilation produces better forecasts than all observations within the full volume. Assimilating the full-volume scan increases the number of potential spurious correlations arising between the forecast metric and radial velocity observation induced state perturbations, which may degrade the forecast metric accuracy.

Benchmarking the Raw Model-Generated Background Forecast in Rapidly Updated Surface Temperature Analyses. Part II: Gridded Benchmark

This is the second part of a series on benchmarking raw 1-h high-resolution numerical weather prediction surface-temperature forecasts from NOAA’s High-Resolution Rapid Refresh (HRRR) system. Such 1-h forecasts are commonly used to underpin the background for an hourly updated surface temperature analysis. The benchmark in this article was produced through a gridded statistical interpolation procedure using only surface observations and a diurnally, seasonally dependent gridded surface temperature climatology. The temporally varying climatologies were produced by synthesizing high-resolution monthly gridded climatologies of daily maximum and minimum temperatures over the contiguous United States with yearly and diurnally dependent estimates of the station-based climatologies of surface temperature. To produce a 1-h benchmark forecast, for a given hour of the day, say 0000 UTC, the gridded climatology was interpolated to station locations and then subtracted from the observations. These station anomalies were statistically interpolated to produce the 0000 UTC gridded anomaly. This anomaly pattern was continued for 1 h and added to the 0100 UTC gridded climatology to generate the 0100 UTC gridded benchmark forecast. The benchmark is thus a simple 1-h persistence of the analyzed deviations from the diurnally dependent climatology. Using a cross-validation procedure with July 2015 and August 2018 data, the gridded benchmark provided competitive, relatively unbiased 1-h surface temperature forecasts relative to the HRRR. Benchmark forecasts were lower in error and bias in 2015, but the HRRR system was highly competitive or better than the gridded benchmark in 2018. Implications of the benchmarking results are discussed, as well as potential applications of the simple benchmarking procedure to data assimilation.

Benchmarking the Raw Model-Generated Background Forecast in Rapidly Updated Surface Temperature Analyses. Part I: Stations

High-quality, high-resolution, hourly unbiased surface (2 m) temperature analyses are needed for many applications, including training and validation of statistical postprocessing applications. These temperature analyses are often generated through data assimilation procedures, whereby a background short-range gridded forecast is adjusted to newly available observations. Even with frequent updates to newly available observations, surface-temperature analysis errors and biases can be comparatively large relative to errors and biases of midtropospheric variables, especially over land, despite more near-surface in situ observations. Larger near-surface errors may have several causes, including biased background forecasts and the spatial heterogeneity of surface temperatures that results from subgrid-scale surface, vegetation, land-use, and terrain variations. Are biased raw background forecasts the predominant cause of surface temperature analysis errors? Part I of this two-part series describes a simple benchmark for evaluating the error characteristics of short-term (1 h) raw model background surface temperature forecasts. For stations with a relatively complete time series of data, it is possible to generate an hourly, diurnally, and seasonally dependent observation climatology at a station. The deviation of the current hour’s temperature observation with respect to this hour’s and Julian day’s climatology is added to the climatology for the next hour. For contiguous U.S. stations in July 2015, the station benchmark was lower in error than interpolated 1-h high-resolution numerical predictions of surface temperature from NOAA’s High-Resolution Rapid Refresh (HRRR) system, although not including full postprocessing. For August 2018, 1-h HRRR forecasts were much improved when tested against the station benchmark.

Verification against perturbed analyses and observations

It has long been known that verification of a forecast against the sequence of analyses used to produce those forecasts can under-estimate the magnitude of forecast errors. Here we show that under certain conditions the verification of a short-range forecast against a perturbed analysis coming from an ensemble data assimilation scheme can give the same root-mean-square error as verification against the truth. This means that a perturbed analysis can be used as a reliable proxy for the truth. However, the conditions required for this result to hold are rather restrictive: the analysis must be optimal, the ensemble spread must be equal to the error in the mean, the ensemble size must be large and the forecast being verified must be the background forecast used in the data assimilation. Although these criteria are unlikely to be met exactly it becomes clear that for most cases verification against a perturbed analysis gives better results than verification against an unperturbed analysis. We demonstrate the application of these results in a idealised model framework and a numerical weather prediction context. In deriving this result we recall that an optimal (Kalman) analysis is one for which the analysis increments are uncorrelated with the analysis errors.

The Transbaikalia Rivers’ Many-year Runoff Regime: Analysis and Background Forecast.

Long-term changes of the river runoff in Transbaikalia have been discussed. The runoff fluctuations are cyclic by character and depend upon many-year changes of atmospheric precipitations. Prolonged trends of the runoff increase or decrease have not been revealed. According to the forecast, the runoff exceeding the standard value will dominate till 2021–2023.

A Hybrid ETKF–3DVAR Data Assimilation Scheme for the WRF Model. Part II: Real Observation Experiments

The hybrid ensemble transform Kalman filter–three-dimensional variational data assimilation (ETKF–3DVAR) system developed for the Weather Research and Forecasting (WRF) Model was further tested with real observations, as a follow-up for the observation system simulation experiment (OSSE) conducted in Part I. A domain encompassing North America was considered. Because of limited computational resources and the large number of experiments conducted, the forecasts and analyses employed relatively coarse grid spacing (200 km) to emphasize synoptic scales. As a first effort to explore the new system with real observations, relatively sparse observation datasets consisting of radiosonde wind and temperature during 4 weeks of January 2003 were assimilated. The 12-h forecasts produced by the hybrid analysis produced less root-mean-square error than the 3DVAR. The hybrid improved the forecast more in the western part of the domain than the eastern part. It also produced larger improvements in the upper troposphere. The overall magnitude of the ETKF ensemble spread agreed with the overall magnitude of the background forecast error. For individual variables and layers, the consistency between the spread and the error was less than the OSSE in Part I. Given the coarse resolution and relatively sparse observation network adopted in this study, caution is warranted when extrapolating these results to operational applications. A case study was also performed to further understand a large forecast improvement of the hybrid during the 4-week period. The flow-dependent adjustments produced by the hybrid extended a large distance into the eastern Pacific data-void region. The much improved analysis and forecast by the hybrid in the data void subsequently improved forecasts downstream in the region of verification. Although no moisture observations were assimilated, the hybrid updated the moisture fields flow dependently through cross-variable covariances defined by the ensemble, which improved the forecasts of cyclone development.

Assimilating Vortex Position with an Ensemble Kalman Filter

Observations of hurricane position, which in practice might be available from satellite or radar imagery, can be easily assimilated with an ensemble Kalman filter (EnKF) given an operator that computes the position of the vortex in the background forecast. The simple linear updating scheme used in the EnKF is effective for small displacements of forecasted vortices from the true position; this situation is operationally relevant since hurricane position is often available frequently in time. When displacements of the forecasted vortices are comparable to the vortex size, non-Gaussian effects become significant and the EnKF’s linear update begins to degrade. Simulations using a simple two-dimensional barotropic model demonstrate the potential of the technique and show that the track forecast initialized with the EnKF analysis is improved. The assimilation of observations of the vortex shape and intensity, along with position, extends the technique’s effectiveness to larger displacements of the forecasted vortices than when assimilating position alone.