The urban heat island (UHI), together with summertime heat waves, foster's biophysical hazards such as heat stress, air pollution, and associated public health problems. Mitigation strategies such as increased vegetative cover and higher albedo surface materials have been proposed. Atlanta, Georgia, is often affected by extreme heat, and has recently been investigated to better understand its heat island and related weather modifications. The objectives of this research were to (1) characterize temporal variations in the magnitude of UHI around Metro Atlanta area, (2) identify climatological attributes of the UHI under extremely high temperature conditions during Atlanta's summer (June, July, and August) period, and (3) conduct theoretical numerical simulations to quantify the first-order effects of proposed mitigation strategies. Over the period 1984-2007, the climatological mean UHI magnitude for Atlanta-Athens and Athens-Monticello was 1.31 and 1.71°C, respectively. There were statistically significant minimum temperature trends of 0.70°C per decade at Athens and -1.79°C per decade at Monticello while Atlanta's minimum temperature remained unchanged. The largest (smallest) UHI magnitudes were in spring (summer) and may be coupled to cloud-radiative cycles. Heat waves in Atlanta occurred during 50% of the years spanning 1984-2007 and were exclusively summertime phenomena. The mean number of heat wave events in Atlanta during a given heat wave year was 1.83. On average, Atlanta heat waves lasted 14.18 days, although there was quite a bit of variability (standard deviation of 9.89). The mean maximum temperature during Atlanta's heat waves was 35.85°C. The Atlanta-Athens UHI was not statistically larger during a heat wave although the Atlanta-Monticello UHI was. Model simulations captured daytime and nocturnal UHIs under heat wave conditions. Sensitivity results suggested that a 100% increase in Atlanta's surface vegetation or a tripling of its albedo effectively reduced UHI surface temperature. However, from a mitigation and technological standpoint, there is low feasibility of tripling

Heavy rainfall, which usually occurs over a certain concentrated period with a large amount of precipitation over an event, can cause natural disasters such as torrents and debris flows, leading to human casualties and huge losses of property. Additionally, with rapid urbanization and the restricted surface waterlogging controls in cities, heavy rainfall can exaggerate urban waterlogging and traffic congestion, which has become common in recent years and has attracted much attention (M. Wu et al., 2019). China, which is strongly affected by monsoons, has unique precipitation characteristics, which have been extensively studied. Most previous precipitation studies have been based on daily or monthly precipitation data, which were used to investigate spatiotemporal distribution characteristics over longer time scales (Gong & Ho, 2002; R. Wu et al., 2010). In recent years, with the availability of refined observations, higher-resolution hourly precipitation data have been used to study on hourly extreme rainfall (

A modular extensible framework for conducting observing system simulation experiments (OSSEs) has been developed with the goals of 1) supporting decision-makers with quantitative assessments of proposed observing systems investments, 2) supporting readiness for new sensors, 3) enhancing collaboration across the community by making the most up-to-date OSSE components accessible, and 4) advancing the theory and practical application of OSSEs. This first implementation, the Community Global OSSE Package (CGOP), is for short- to medium-range global numerical weather prediction applications. The CGOP is based on a new mesoscale global nature run produced by NASA using the 7-km cubed sphere version of the Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model and the January 2015 operational version of the NOAA global data assimilation (DA) system. CGOP includes procedures to simulate the full suite of observing systems used operationally in the global DA system, including conventional in situ, satellite-based radiance, and radio occultation observations. The methodology of adding a new proposed observation type is documented and illustrated with examples of current interest. The CGOP is designed to evolve, both to improve its realism and to keep pace with the advance of operational systems.

We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes a deep neural network approach, is designed to incorporate dependence on the atmospheric and surface conditions that impact forward model biases. The approach utilizes collocations of observed Suomi National Polar-orbiting Partnership/Advanced Technology Microwave Sounder (SNPP/ATMS) radiances and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses which are used as input to the Community Radiative Transfer Model (CRTM) forward model to develop training data of radiometric biases. Analysis of the neural network performance indicates that in many channels, the dynamic bias is able to reproduce realistically both the spatial patterns of the original bias and its probability distribution function. Furthermore, retrieval impact experiments on independent data show that, compared with the baseline static bias correction, using the dynamic bias correction can improve temperature and water vapor profile retrievals, particularly in regions with higher Cloud Liquid Water (CLW) amounts. Ocean surface emissivity retrievals are also improved, for example at 23.8 GHz, showing an increase in correlation from 0.59 to 0.67 and a reduction of standard deviation from 0.035 to 0.026.