Quantitative comparisons of satellite observations and cloud models

2011 Fall.Includes bibliographical references.Microwave radiation interacts directly with precipitating particles and can therefore be used to compare microphysical properties found in models with those found in nature. Lower frequencies (< 37 GHz) can detect the emission signals from the raining clouds over radiometrically cold ocean surfaces while higher frequencies (≥ 37 GHz) are more sensitive to the scattering of the precipitating-sized ice particles in the convective storms over high-emissivity land, which lend them particular capabilities for different applications. Both are explored with a different scenario for each case: a comparison of two rainfall retrievals over ocean and a comparison of a cloud model simulation to satellite observations over land. Both the Goddard Profiling algorithm (GPROF) and European Centre for Medium-Range Weather Forecasts (ECMWF) one-dimensional + four-dimensional variational analysis (1D+4D-Var) rainfall retrievals are inversion algorithms based on the Bayes' theorem. Differences stem primarily from the a-priori information. GPROF uses an observationally generated a-priori database while ECMWF 1D-Var uses the model forecast First Guess (FG) fields. The relative similarity in the two approaches means that comparisons can shed light on the differences that are produced by the a-priori information. Case studies have found that differences can be classified into four categories based upon the agreement in the brightness temperatures (Tbs) and in the microphysical properties of Cloud Water Path (CWP) and Rain Water Path (RWP) space. We found a category of special interest in which both retrievals converge to similar Tb through minimization procedures but produce different CWP and RWP. The similarity in Tb can be attributed to comparable Total Water Path (TWP) between the two retrievals while the disagreement in the microphysics is caused by their different degrees of constraint of the cloud/rain ratio by the observations. This situation occurs frequently and takes up 46.9% in the one month 1D-Var retrievals examined. To attain better constrained cloud/rain ratios and improved retrieval quality, this study suggests the implementation of higher microwave frequency channels in the 1D-Var algorithm. Cloud Resolving Models (CRMs) offer an important pathway to interpret satellite observations of microphysical properties of storms. High frequency microwave brightness temperatures (Tbs) respond to precipitating-sized ice particles and can, therefore, be compared with simulated Tbs at the same frequencies. By clustering the Tb vectors at these frequencies, the scene can be classified into distinct microphysical regimes, in other words, cloud types. The properties for each cloud type in the simulated scene are compared to those in the observation scene to identify the discrepancies in microphysics within that cloud type. A convective storm over the Amazon observed by the Tropical Rainfall Measuring Mission (TRMM) is simulated using the Regional Atmospheric Modeling System (RAMS) in a semi-ideal setting, and four regimes are defined within the scene using cluster analysis: the 'clear sky/thin cirrus' cluster, the 'cloudy' cluster, the 'stratiform anvil' cluster and the 'convective' cluster. The relationship between Tb difference of 37 and 85 GHz and Tb at 85 GHz is found to contain important information of microphysical properties such as hydrometeor species and size distributions. Cluster-by-cluster comparison between the observations and the simulations discloses biases in the model including overproduction of supercooled water and large hail particles. The detected biases shed light on how the model should be adjusted to generate more realistic microphysical relationships for each cluster. Guided by the model/observation discrepancies in the 'convective' cloud cluster, a new simulation is performed to provide dynamic adjustments by generating more but smaller hail particles

Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events

An acoustic view of ocean mixing

Knowledge of the parameter K (turbulent diffusivity/"mixing intensity") is a key to understand transport processes of matter and energy in the ocean. Especially the almost vertical component of K across the ocean stratification (diapycnal diffusivity) is vital for research on biogeochemical cycles or greenhouse gas budgets. Recent boost in precision of water velocity data that can be obtained from vessel-mounted acoustic instruments (vmADCP) allows identifying ocean regions of elevated diapycnal diffusivity during research cruises - in high horizontal resolution and without extra ship time needed. This contribution relates acoustic data from two cruises in the Tropical North East Atlantic Oxygen Minimum Zone to simultaneous field observations of diapycnal diffusivity: pointwise measurements by a microstructure profiler as well as one integrative value from a large scale Tracer Release Experiment

Foreword to the special issue on pattern recognition in remote sensing

Cataloged from PDF version of article.The nine papers in this special issue focus on covering different aspects of remote sensing image analysis. © 2012 IEE

Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0

In this study, we present an expansive sensitivity analysis of physics configurations for cloud cover using the Weather Forecasting and Research Model (WRF V3.7.1) on the European domain. The experiments utilize the meteorological part of a large ensemble framework known as the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-met). The experiments first seek the best deterministic WRF physics configuration by simulating over 1,000 combinations of microphysics, cumulus parameterization, planetary boundary layer physics (PBL), surface layer physics, radiation scheme and land surface models. The results on six different test days are compared to CMSAF satellite images from EUMETSAT. We then selectively conduct stochastic simulations to assess the best choice for ensemble forecasts. The results indicate a high variability in terms of physics and parameterization. The combination of Goddard, WSM6, or CAM5.1 microphysics with MYNN3 or ACM2 PBL exhibited the best performance in Europe. For probabilistic simulations, the combination of WSM6 and SBU&ndash;YL microphysics with MYNN2 and MYNN3 showed the best performance, capturing the cloud fraction and its percentiles with 32 ensemble members. This work also demonstrates the capability and performance of ESIAS-met for large ensemble simulations and sensitivity analysis.</p