With the launch of the NOAA-15 satellite in May 1998, a new generation of passive microwave sounders was initiated. The Advanced Microwave Sounding Unit (AMSU), with 20 channels spanning the frequency range from 23-183 GHz, offers enhanced temperature and moisture sounding capability well beyond its predecessor, the Microwave Sounding Unit (MSU). In addition, by utilizing a number of window channels on the AMSU, the National Oceanic and Atmospheric Administration (NOAA) expanded the capability of the AMSU beyond this original purpose and developed a new suite of products that are generated through the Microwave Surface and Precipitation Products System (MSPPS). This includes precipitation rate, total precipitable water, land surface emissivity, and snow cover. Details on the current status of the retrieval algorithms (as of September 2004) are presented. These products are complimentary to similar products obtained from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager (SSMI) and the Earth Observing Aqua Advanced Microwave Scanning Radiometer (AMSR-E). Due to the close orbital equatorial crossing time between NOAA-16 and the Aqua satellites, comparisons between several of the MSPPS products are made with AMSR-E. Finally, several application examples are presented that demonstrate their importance to weather forecasting and analysis, and climate monitoring.

Malaria is a leading cause of mortality worldwide. There is currently conflicting data and interpretation on how variability in climate factors affects the incidence of malaria. This study presents a hierarchical Bayesian modelling framework for the analysis of malaria versus climate factors in West Africa.

This paper presents a snowfall detection algorithm over land from high‐frequency passive microwave measurements. The algorithm computes the probability of snowfall using logistic regression and the principal components of the seven high‐frequency brightness temperature measurements at Atmospheric Technology Microwave Sounder (ATMS) channel frequencies 89 GHz and above. The oxygen absorption channel 6 (53.6 GHz) is utilized as temperature proxy to define the snowfall retrieval domain. Ground truth surface meteorological data including snowfall occurrence were collected over Conterminous U.S. and Alaska during two winter seasons in 2012–2013 and 2013–2014. Statistical analysis of the in situ data matched with ATMS measurements showed that in relatively warmer weather, snowfall tends to be associated with lower high‐frequency brightness temperatures than no snowfall, and the brightness temperatures are negatively correlated with measured snowfall rate. In colder weather conditions, however, snowfall tends to occur at higher microwave brightness temperatures than no‐snowfall, and the brightness temperatures are positively correlated with snowfall rate. The brightness temperature decrease and the negative correlations with snowfall rate in warmer weather are attributed to the scattering effect. It is hypothesized that the scattering effect is insignificant in colder weather due to the predominance of lighter snowfall and emission. Based on these results, a two‐step algorithm is developed that optimizes snowfall detection over these two distinct temperature regimes. Evaluation of the algorithm shows skill in capturing snowfall in variable weather conditions as well as the remaining challenges in the retrieval of lighter and colder snowfall.

The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January–February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA’s Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.