Cezar Kongoli

NOAA operational hydrological products derived from the advanced microwave sounding unit

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.

MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products.

A snowfall detection algorithm over land utilizing high-frequency passive microwave measurements—Application to ATMS

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.

A New Sea-Ice Concentration Algorithm Based on Microwave Surface Emissivities—Application to AMSU Measurements

Passive microwave sea-ice retrieval algorithms are typically tuned to brightness temperature measurements with simple treatments of weather effects. The new technique presented is a two-step algorithm that variationally retrieves surface emissivities from microwave remote sensing observations, followed by the retrieval of sea-ice concentration from surface emissivities. Surface emissivity spectra are interpreted for determining sea-ice fraction by comparison with a catalog of sea-ice emissivities to find the closest match. This catalog was computed off-line from known ocean, first-year, and multiyear sea-ice reference emissivities for a range of fractions. The technique was adjusted for application to the Advanced Microwave Sounding Unit (AMSU)/Microwave Humidity Sensor observations, and its performance was compared to the National Oceanic and Atmospheric Administration (NOAA)s AMSU heritage sea-ice algorithm and to NOAAs operational Interactive Multi-sensor Snow and Ice Mapping System taken as ground truth. Assessment results indicate a performance that is superior to the heritage algorithm particularly over multiyear ice and during the warm season.

NOAA satellite‐derived hydrological products prove their worth

Satellite observations are particularly important for monitoring the global changes of atmospheric and surface features. For many parameters, satellite measurements are the only means of obtaining this information, particularly over the oceans and sparsely-populated land areas. For example, multi-spectral measurements from both geostationary and polar-orbiting satellites are key components of the Global Precipitation Climatology Project (GPCP) [Huffman et al., 1996], which has measured global rainfall for over 20 years. In addition, the longstanding National Oceanic and Atmospheric Administration (NOAA)-based Northern Hemispheric snow cover climatology has relied almost solely on satellite observations that are interpreted by satellite analysts [Robinson et al., 1993].

A 1DVAR‐based snowfall rate retrieval algorithm for passive microwave radiometers

Snowfall rate retrieval from space-borne passive microwave (PMW) radiometers has gained momentum in recent years. PMW can be so utilized because of its ability to sense in-cloud precipitation. A physically-based, overland snowfall rate (SFR) algorithm has been developed using measurements from the Advanced Microwave Sounding Unit-A (AMSU-A)/Microwave Humidity Sounder (MHS) sensor pair and the Advanced Technology Microwave Sounder (ATMS). Currently, these instruments are aboard five polar-orbiting satellites, namely NOAA-18, NOAA-19, Metop-A, Metop-B, and Suomi-NPP. The SFR algorithm relies on a separate snowfall detection (SD) algorithm that is composed of a satellite-based statistical model and a set of numerical weather prediction (NWP) model-based filters. There are four components in the SFR algorithm itself: cloud properties retrieval, computation of ice particle terminal velocity, ice water content (IWC) adjustment, and the determination of snowfall rate. The retrieval of cloud properties is the foundation of the algorithm and is accomplished using a one-dimensional variational (1DVAR) model. An existing model is adopted to derive ice particle terminal velocity. Since no measurement of cloud ice distribution is available when SFR is retrieved in near real-time, such distribution is implicitly assumed by deriving an empirical function that adjusts retrieved SFR towards radar snowfall estimates. Finally, SFR is determined numerically from a complex integral. The algorithm has been validated against both radar and ground observations of snowfall events from the Contiguous United States with satisfactory results. Currently, the SFR product is operationally generated at the National Oceanic and Atmospheric Administration (NOAA) and can be obtained from that organization.

A multi-source interactive analysis approach for Northern hemispheric snow depth estimation

This paper presents a new approach to operational snow depth analysis. The analysis uses 2-dimensional optimal interpolation to blend various snow depth data weighted against their errors relative to a first guess and spatial correlations. The blended analysis is applied operationally within the National Oceanic and Atmospheric Administration (NOAA)s Interactive Multi-Sensor Snow and Ice Mapping System (IMS) at its snow cover-classified grid points over the Northern Hemisphere. The 4-km snow depth estimates are blended from satellite-derived estimates of the Advanced Microwave Scanning Radiometer 2 (AMSR2) or the Advanced Technology Microwave Sounder (ATMS), in-situ surface reports and analyst estimates. Unique to the production is the application of snow depth estimates with the associated confidence values generated interactively from the analyst that are also ingested into the objective analysis and fully consistent with optimal interpolation method.

An intercomparison of two passive microwave algorithms for snowfall detection over Europe

The proposed work aims to enhance the capabilities of the passive microwave measurements on board NOAA and MetOp satellites for snowfall identification. Two independent methods based on the same sensors (AMSU/MHS) were applied and qualitatively inter-compared during snowstorms over Europe by using the NIMROD radar network as ground truth. The first method developed at NOAA is a statistical algorithm that computes the probability of snowfall using a logistic regression and the principal components of the high frequency brightness temperature measurements at MHS or ATMS channel frequencies 89 GHz and above. The second approach for snowfall detection (183-WSLSF) developed by CNR-ISAC is a prototype based on the preexistent 183-WSL retrieval method. By considering as limit of snowfall formation the rainy clouds located from a few hundred meters to 5-6 km, the 183-WSLSF combines channel sensitivities from 90 and 190 GHz to identify snowfall areas in the precipitating cores.

The retrievals of effective grain size and snow water equivalent from variationally-retrieved microwave surface emissivities

This study introduces a new technique for the estimation of the snow effective grain size and water equivalent based on the microwave surface emissivity spectra retrieved from a one-dimensional variational retrieval system and a microwave snow emissivity model. The microwave emissivity model is derived analytically from the dense media radiative transfer theory. The model snow physical parameters include the effective grain size, volume fraction and depth. The two-steps algorithm is based on variationally retrieving the emissivity spectrum from microwave remote sensing observations, followed by the estimation of the closest emissivity spectrum from a catalog to determine the snow water equivalent and the effective grain size. This catalog was computed off-line using the microwave emissivity model for realistic ranges of the effective snow parameters. Qualitative inspection of variationally retrieved emissivities and the snow parameters show large-scale consistency. The performance of this physically-based retrieval technique is quantitatively assessed against snow water equivalent measurements and against an empirical brightness temperature-based algorithm.