Assessment of a Broadband Differential Absorption Lidar (DIAL) for use in a ground-based measurement network

<p>Convective-scale forecasts require more detailed and continuous observational data of thermodynamic profiles and wind profiles in the atmospheric boundary layer (ABL) than currently provided. In order to meet these data requirements in the future, DWD evaluates various surface remote sensing systems targeted on ABL-profiling for routine network operation.</p> <p>One of the candidate systems in operation at the Observatory Lindenberg is a new pre-production broadband DIAL from Vaisala. DIAL instruments are well-established in research activities, but this instrument is developed for operationally providing water vapor profile observations in the ABL during all weather conditions. We present evaluation results of the DIAL’s operational performance regarding the quality of the water vapor profiles and report on its ability to monitor sub-grid scale processes, such as convection and associated weather phenomena. This includes comparisons with radiosounding observations (4 per day) over at least one year of continuous observations and additional comparisons with Raman lidar for a three-month period during summer 2021. Furthermore, we provide observation-minus-background statistics between the DIAL and the ICON limited area model (ICON-LAM) to evaluate the model performance, e.g. under convection, and to identify observational error sources.</p> <p>This contribution provides knowledge regarding the operational viability of the new pre-production broadband DIAL, its value for monitoring water vapour profiles 24/7 and ABL processes for future model applications.</p>

Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction

Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of the non-Gaussianity of forecast error distributions at 1 km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency and observation number. Regional numerical weather prediction experiments are performed with the SCALE (Scalable Computing for Advanced Library and Environment) model and the LETKF (local ensemble transform Kalman filter) assimilating phased array radar observations every 30 s. The results show that non-Gaussianity develops rapidly within convective clouds and is sensitive to the DA frequency and the number of assimilated observations. The non-Gaussianity is reduced by up to 40 % when the assimilation window is shortened from 5 min to 30 s, particularly for vertical velocity and radar reflectivity.

Stochastic Downscaling to Chaotic Weather Regimes using Spatially Conditioned Gaussian Random Fields with Adaptive Covariance

Downscaling aims to link the behaviour of the atmosphere at fine scales to properties measurable at coarser scales, and has the potential to provide high resolution information at a lower computational and storage cost than numerical simulation alone. This is especially appealing for targeting convective scales, which are at the edge of what is possible to simulate operationally. Since convective scale weather has a high degree of independence from larger scales, a generative approach is essential. We here propose a statistical method for downscaling moist variables to convective scales using conditional Gaussian random fields, with an application to wet bulb potential temperature (WBPT) data over the UK. Our model uses an adaptive covariance estimation to capture the variable spatial properties at convective scales. We further propose a method for the validation, which has historically been a challenge for generative models.

Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of convective storms

The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional and convective scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. Results show that a baseline RRFS run without data assimilation is able to represent the observed convection, but with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection and precipitation are overforecast in most forecast hours when using planetary boundary layer pseudo-observations, but the root mean square error and bias of the 2 h forecast of 2 m dew point temperature are reduced by 1.6 K during the afternoon hours. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.