Evaluation Of Cloud Properties Research Articles

Evaluation of cloud properties from reanalyses over East Asia with a radiance-based approach

Abstract. Extensive observational and numerical investigations have been performed to better characterize cloud properties. However, due to the large variations in cloud spatiotemporal distributions and physical properties, quantitative depictions of clouds in different atmospheric reanalysis datasets are still highly uncertain. A radiance-based evaluation approach is introduced and performed to evaluate the quality of cloud properties from reanalysis datasets. The China Meteorological Administration reanalysis (CRA); the ECMWF fifth-generation reanalysis (ERA5); and the Modern-Era Retrospective analysis for Applications, Version 2 (MERRA-2), i.e., those reanalyses providing sufficient cloud information, are considered. To avoid the influence of assumptions and uncertainties on satellite retrieval algorithms, forward radiative transfer simulations are used as a bridge to translate the reanalyses to corresponding radiances that are expected to be observed by satellites. The simulated reflectances and brightness temperatures (BTs) are directly compared with observations from the Advanced Himawari Imager onboard the Himawari-8 satellite in the East Asia region. We find that the simulated reflectances and BTs based on CRA and ERA5 are close to each other. CRA represents the total and midlayer cloud cover better than the other two datasets, and ERA5 depicts deep-convection structures more closely than CRA does. Comparisons of the simulated and observed BT differences suggest that water clouds are generally overestimated in ERA5 and MERRA-2, and MERRA-2 also overestimates the ice clouds over cyclone centers. Overall, clouds from CRA, ERA5, and MERRA-2 show their own advantages in different aspects. The ERA5 reanalysis has the best capability to represent the cloudy atmospheres over East Asia, and the CRA representations are close to those in ERA5.

Evaluation of cloud properties in the NCEP CFSv2 model and its linkage with Indian summer monsoon

Cloud fraction, which varies greatly among general circulation models, plays a crucial role in simulation of Indian summer monsoon rainfall (ISMR). The NCEP Climate Forecast System version 2 (CFSv2) model is evaluated in terms of its simulation of cloud fraction, cloud condensate, outgoing longwave radiation (OLR), and tropospheric temperature (TT). Biases in these simulated quantities are computed using observations from CALIPSO and reanalysis data from MERRA. It is shown that CFSv2 underestimates (overestimates) high- (mid-) level clouds. The cloud condensate is also examined to see its impact on different types of clouds. The upper-level cloud condensate is underestimated, particularly during the summer monsoon period, which leads to a cold TT and a dry precipitation bias. The unrealistically weak TT gradient between ocean and land is responsible for the underestimation of ISMR. The model-simulated OLR is overestimated which depicts the weaker convective activity. A large underestimate of precipitable water is also seen along the cross-equatorial flow and particularly over the Indian land region collocated with a dry precipitation bias. The linkages among cloud microphysical, thermodynamical, and dynamical processes are identified here. Thus, this study highlights the importance of cloud properties, a major cause of uncertainty in CFSv2, and also proposes a pathway for improvements in its simulation of the Indian summer monsoon.

Evaluation of cloud properties in the NOAA/NCEP global forecast system using multiple satellite products

Knowledge of cloud properties and their vertical structure is important for meteorological studies due to their impact on both the Earth’s radiation budget and adiabatic heating within the atmosphere. The objective of this study is to evaluate bulk cloud properties and vertical distribution simulated by the US National Oceanic and Atmospheric Administration National Centers for Environmental Prediction Global Forecast System (GFS) using three global satellite products. Cloud variables evaluated include the occurrence and fraction of clouds in up to three layers, cloud optical depth, liquid water path, and ice water path. Cloud vertical structure data are retrieved from both active (CloudSat/CALIPSO) and passive sensors and are subsequently compared with GFS model results. In general, the GFS model captures the spatial patterns of hydrometeors reasonably well and follows the general features seen in satellite measurements, but large discrepancies exist in low-level cloud properties. More boundary layer clouds over the interior continents were generated by the GFS model whereas satellite retrievals showed more low-level clouds over oceans. Although the frequencies of global multi-layer clouds from observations are similar to those from the model, latitudinal variations show discrepancies in terms of structure and pattern. The modeled cloud optical depth over storm track region and subtropical region is less than that from the passive sensor and is overestimated for deep convective clouds. The distributions of ice water path (IWP) agree better with satellite observations than do liquid water path (LWP) distributions. Discrepancies in LWP/IWP distributions between observations and the model are attributed to differences in cloud water mixing ratio and mean relative humidity fields, which are major control variables determining the formation of clouds.

The role of precipitation size distributions in km‐scale NWP simulations of intense precipitation: evaluation of cloud properties and surface precipitation

AbstractWe investigate the sensitivity of simulated cloud properties and surface precipitation to assumptions regarding the size distributions of the precipitating hydrometeors in a one‐moment bulk microphysics scheme. Three sensitivity experiments were applied to two composites of 15 convective and 15 frontal stratiform intense precipitation events observed in a coastal midlatitude region (Belgium), which were evaluated against satellite‐retrieved cloud properties and radar‐rain‐gauge derived surface precipitation. It is found that the cloud optical thickness distribution was well captured by all experiments, although a significant underestimation of cloudiness occurred in the convective composite. The cloud‐top‐pressure distribution was improved most by more realistic snow size distributions (including a temperature‐dependent intercept parameter and non‐spherical snow for the calculation of the slope parameter), due to increased snow depositional growth at high altitudes. Surface precipitation was far less sensitive to whether graupel or hail was chosen as the rimed ice species, as compared to previous idealized experiments. This smaller difference in sensitivity could be explained by the stronger updraught velocities and higher freezing levels in the idealized experiments compared to typical coastal midlatitude environmental conditions. Copyright © 2012 Royal Meteorological Society