Abstract
Numerical weather prediction (NWP) models are moving towards km-scale (and smaller) resolutions in order to forecast high-impact weather. As the resolution of NWP models increase the need for high-resolution observations to constrain these models also increases. A major hurdle to the assimilation of dense observations in NWP is the presence of non-negligible observation error correlations (OECs). Despite the difficulty in estimating these error correlations, progress is being made, with centres around the world now explicitly accounting for OECs in a variety of observation types. This paper explores how to make efficient use of this potentially dramatic increase in the amount of data available for assimilation. In an idealised framework it is illustrated that as the length-scales of the OECs increase the scales that the analysis is most sensitive to the observations become smaller. This implies that a denser network of observations is more beneficial with increasing OEC length-scales. However, the computational and storage burden associated with such a dense network may not be feasible. To reduce the amount of data, a compression technique based on retaining the maximum information content of the observations can be used. When the OEC length-scales are large (in comparison to the prior error correlations), the data compression will select observations of the smaller scales for assimilation whilst throwing out the larger scale information. In this case it is shown that there is a discrepancy between the observations with the maximum information and those that minimise the analysis error variances. Experiments are performed using the Ensemble Kalman Filter and the Lorenz-1996 model, comparing different forms of data reduction. It is found that as the OEC length-scales increase the assimilation becomes more sensitive to the choice of data reduction technique.
Highlights
(e.g. Advanced Himawari Imager onboard Himawari-8, In numerical weather prediction (NWP), the assimilation The Infrared Sounder onboard MTG-S) and phased of observations and prior information (commonly array weather Radars (Miyoshi et al, 2016)
A major hurdle to the assimilation of dense observations in Numerical weather prediction (NWP) is the presence of non-negligible observation error correlations (OECs)
Data assimilation (DA) has include issues with data storage, computational time proven to be essential for accurate weather forecasting by and complications caused by correlated errors, which providing the initial conditions for NWP
Summary
(e.g. Advanced Himawari Imager onboard Himawari-8, In numerical weather prediction (NWP), the assimilation The Infrared Sounder onboard MTG-S) and phased of observations and prior information (commonly array weather Radars (Miyoshi et al, 2016). Rapid up-date forecasting systems are under development at centres such as RIKEN-Center for Computational Science, that perform DA at resolutions up to 100 m on a local domain in order to produce 30 min forecasts (Miyoshi et al, 2016) At these shorter lead times, the influence of the observation on the analysis, rather than the forecast, becomes more insightful in defining an optimal data compression strategy. In addition to this it was shown in Fowler et al (2018) that the analysis RMSE (or analysis error variance) only gives a partial measure of the effect of allowing for OECs in the assimilation, and as will be demonstrated further here, is more sensitive to large scale corrections than small-scale corrections.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have