Radiance‐Based Evaluation of WRF Cloud Properties Over East Asia: Direct Comparison With FY‐2E Observations

Abstract

Radiance‐based comparison between model‐simulated and satellite‐observed atmosphere shows its unique advantage for model evaluation. This study couples a fast radiative transfer model, namely, the community radiative transfer model, and the Weather Research and Forecasting (WRF) to generate synthetic satellite observed infrared (IR) brightness temperatures (BTs) over East Asia. Simulated IR BTs are used to evaluate WRF cloud properties by direct comparison with observations from the Visible and Infrared Spin‐Scan Radiometer (onboard the FengYun‐2E geostationary satellite). A deep cyclone over North China is investigated as our case study. The simulated and observed BTs in the 6.8‐μm channel demonstrate agreement with an averaged bias of <3 K, whereas the performances in two IR window channels are relatively poor with averaged BT differences almost 10 K. For typical precipitation regions, BT differences between the 6.8‐ and 10.8‐μm channels indicate that the WRF simulation underestimates low‐ to midlevel clouds but reproduces high‐level clouds better. Overall, the WRF has solid capabilities to capture the cloud characteristics broadly, whereas some limitations are shown in the cloud temporal and variation cloud cover, especially 3 days after the WRF initialization.

Evaluation of synthetic satellite images computed from radiative transfer models over a region of South America using WRF and GOES‐13/16 observations

“…Figure 6b shows that low-to mid-level clouds are especially problematic for the WRF+CRTM BTs, but there are also problems with upper-level clouds as discussed with Figure 3f. Similar results were obtained by Jankov et al (2011), Cintineo et al (2014 and Yao et al (2018). An additional category with BTDs > −2 K are used to represent the highest clouds and can indicate the presence of overshooting tops (OTs, not shown), the ratio of CRTM/GOES pixels under BTDs > −2 K is always above 1, and on average close to 5.…”

“…(2014) and Yao et al . (2018). An additional category with BTDs K are used to represent the highest clouds and can indicate the presence of overshooting tops (OTs, not shown), the ratio of CRTM/GOES pixels under BTDs K is always above 1, and on average close to 5.…”

“…A traditional approach in NWP evaluation is the model‐to‐satellite approach (i.e., Morcrette, 1991; Cintineo et al ., 2014; Thompson et al ., 2016; Galligani et al ., 2017), where brightness temperatures (BTs) are simulated with the aid of a radiative transfer (RT) model coupled with an NWP model, and compared with real satellite observations. Synthetic infrared satellite imagery provides an integrated view of the atmospheric model variables, a simple way to identify important mesoscale features, and the means of comparing NWP model outputs with real observations (e.g., Grasso et al ., 2008; Otkin et al ., 2009; Yao et al ., 2018). A robust RT model is required to calculate synthetic images.…”

Satellite Observation for Evaluating Cloud Properties of the Microphysical Schemes in Weather Research and Forecasting Simulation: A Case Study of the Mei-Yu Front Precipitation System

“…Liu et al [36] used the simulated satellite observations to evaluate the performance of the cloud distribution of each cloud type from Taiwan's Central Weather Bureau global forecast system using two different cumulus schemes with MTSAT-2 and identified characteristics of these cumulus schemes. Yao et al [37] used FY-2E to evaluate the performance of the cloud in Weather Research and Forecasting (WRF) using a single microphysical scheme, and they illustrated the characteristics of different cloud types over mainland China.…”

A Supercooled Water Cloud Detection Algorithm Using Himawari‐8 Satellite Measurements

“…Roskovensky et al (2011) analyzed the sensitivity of SWCs to cloud microphysical and thermodynamic properties from Moderate Resolution Imaging Spectroradiometer (MODIS) data and developed an SWC content detection algorithm, with uncertainties up to approximately 30% due to individual changes in cloud properties and corrections in the cloud water path. With the development of satellite remote sensing technology, geostationary satellites provide an opportunity for the continuous and widespread recognition of potential SWCs (Lin et al, 1998;Yao et al, 2018). Ellrod and Nelson (1996) presented the potential for using geostationary satellite data to detect SWCs.…”