Macroscopic cloud properties in the WRF NWP model: An assessment using sky camera and ceilometer data

Abstract

The ability of six microphysical parameterizations included in the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model to represent various macroscopic cloud characteristics at multiple spatial and temporal resolutions is investigated. In particular, the model prediction skills of cloud occurrence, cloud base height, and cloud cover are assessed. When it is possible, the results are provided separately for low‐, middle‐, and high‐level clouds. The microphysical parameterizations assessed are WRF single‐moment six‐class, Thompson, Milbrandt‐Yau, Morrison, Stony Brook University, and National Severe Storms Laboratory double moment. The evaluated macroscopic cloud properties are determined based on the model cloud fractions. Two cloud fraction approaches, namely, a binary cloud fraction and a continuous cloud fraction, are investigated. Model cloud cover is determined by overlapping the vertically distributed cloud fractions following three different strategies. The evaluation is conducted based on observations gathered with a ceilometer and a sky camera located in Jaén (southern Spain). The results prove that the reliability of the WRF model mostly depends on the considered cloud parameter, cloud level, and spatiotemporal resolution. In our test bed, it is found that WRF model tends to (i) overpredict the occurrence of high‐level clouds irrespectively of the spatial resolution, (ii) underestimate the cloud base height, and (iii) overestimate the cloud cover. Overall, the best cloud estimates are found for finer spatial resolutions (1.3 and 4 km with slight differences between them) and coarser temporal resolutions. The roles of the parameterization choice of the microphysics scheme and the cloud overlapping strategy are, in general, less relevant.

A Methodology for Verifying Cloud Forecasts with VIIRS Imagery and Derived Cloud Products—A WRF Case Study

“…In the WRF system, QCLOUD (i.e., liquid water mixing ratio (LWMR)) is converted into cloud fractional coverage at each WRF output (eta or pressure) level, as described by Equation (4) in Xu and Randall [16]. Total cloud fractional coverage can then be derived from the cloud fractional cover layers using different methods as described by the Unified Post Processing (UPP) User's Guide, [17][18][19][20]. Since this study focuses on single-layered water clouds in the truth data, ideally the values for layered cloud cover fraction and total cloud cover fraction in WRF should be the same.…”

A new methodology for PBL height estimations based on lidar depolarization measurements: analysis and comparison against MWR and WRF model-based results

“…temperature, wind and humidity profiles) of the 1 km domain are analysed. The initial and boundary conditions for the WRF model runs are taken from the NCEP (National Centers for Environmental Prediction) high-resolution Global Forecast System data set (http://www.emc.ncep.noaa.gov) every 6 h. The choice of the model's physical parameterization is based on the results of previous evaluation studies conducted in the study area (Arbizu-Barrena et al, 2015). Particularly, the Mellor-Yamada-Nakanishi-Niino level 2.5 model is selected for the PBL parameterization (Nakanishi and Niino, 2009).…”

Comparison of global horizontal irradiance forecasts based on numerical weather prediction models with different spatio‐temporal resolutions

“…These phase shifts are caused by displacement errors in cloud prediction (see e.g. ). Even small errors in cloud position can result in large errors for high resolution forecasts resolving also small scale cloud features—which is often referred to as ‘double penalty’ effect.…”

“…In order to assess the benefit of these models and to mitigate the impact of the double penalty effect, the use of additional verifications methods complementing traditional point wise evaluation is recommended in several studies (e.g. ). These include various spatial and temporal averaging approaches.…”