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Kim, Min-Jeong

Publications and source records attributed to Kim, Min-Jeong.

16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Impact of Satellite Microwave Radiance Data Assimilation on GEOS Atmospheric Analysis and Forecasts in Tropics

The NASA Global Modeling and Assimilation Office (GMAO) has developed a system to assimilate all-sky microwave radiance data in the Goddard Earth Observing System (GEOS). The system provides additional constraints on the analysis process near the storm regions and adjusts the geophysical parameters such as precipitation, cloud, moisture, surface pressure, and wind by combining information from microwave radiance measurements and model forecasts in an optimal manner. The system proved that assimilating the all-sky microwave radiance data improve the GEOS atmospheric analyses and forecasts. This all-sky data framework has been included in the GEOS Forward Processing (FP) system and currently assimilating all-sky GMI data in real-time for GEOS global analysis and forecast production at the GMAO. This presentation describes the methodologies to assimilate cloud- and precipitation-affected microwave radiances in GEOS data assimilation system based on hybrid four-dimensional ensemble-variational (4D-EnVar) configuration. In the current operational GEOS-FP system, the addition of all-sky GMI radiances has the largest impact in the Tropics. Specific humidity is significantly improved in the short term (0-72 hours) forecasts. Similar improvements are seen in the Tropical and lower tropospheric temperature and winds. More detailed results on all-sky microwave radiance data impact on GEOS analysis and forecasts are discussed in this presentation.

Kim, Min-Jeong↗
NASA NTRS2020

Assimilating GCOM-W1 AMSR2 and TRMM TMI Radiance Data in GEOS Analysis and Reanalysis

The Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) observed the Earth in lower latitudes between 1997 - 2015. Its conical-scan radiometer has nine channels and measured microwave radiances between 10 and 89 GHz. These data provide information on atmospheric temperature, humidity, clouds, precipitation, as well as sea surface temperature. Radiance data from other microwave radiometers such as Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) onboard various Defense Meteorological Satellite Program (DMSP) satellites are assimilated in clear-sky conditions in the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its version 2 (MERRA-2) data sets at the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. The GMAO's Hybrid 4D-EnVar-based Atmospheric Data Assimilation System (ADAS) is enhanced with an all-sky microwave radiance data assimilation capability in the real-time GEOS-Forward Processing (FP) system. Currently, the FP system assimilates Global Precipitation Measurement (GPM) microwave imager (GMI) radiance data utilizing this all-sky capability, and is being extended to use more all-sky data from other microwave radiometers. In this presentation, we will focus on impacts of all-sky TMI radiance data on GEOS analyses of atmospheric moisture, precipitation and other fields, and discuss their applications for future GEOS reanalyses.

Jin, Jianjun↗
NASA NTRS2020

Assimilating All-Sky Microwave Radiance Data to Improve NASA GEOS Forecasts and Analysis

The NASA Global Modeling and Assimilation Office (GMAO) has been pursuing efforts to utilize all-sky (clear+cloudy+precipitating) MW radiance data and has developed a system to assimilate all-sky GPM Microwave Imager (GMI) radiance data in the Goddard Earth Observing System (GEOS) during the last PMM funding period. The system provides additional constraints on the analysis process near the storm regions and adjusts the geophysical parameters such as precipitation, cloud, moisture, surface pressure, and wind by combining information from GMI radiance measurements and model forecasts in an optimal manner. The system proved that assimilating the GMI all-sky radiance data improve the GEOS atmospheric analyses and forecasts. This all-sky data framework has been included in the GEOS Forward Processing (FP) system since July 11, 2018 and assimilates all-sky GMI data in real-time for GEOS global analysis and forecast production at the GMAO. We are currently extending this all-sky GMI radiance data assimilation system to assimilate more all-sky MW radiance data from other sensors such as the Microwave Humidity Sounder (MHS), the Advanced Technology Microwave Sounder (ATMS), the Special Sensor Microwave Imager/Sounder (SSMIS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometery (SAPHIR) onboard the GPM constellation spacecrafts. Preliminary results from this extended all-sky system show increased benefit from cloud- and precipitation-affected MW radiances with much larger spatial and temporal coverages compared to the all-sky system assimilating GMI alone and improved GEOS forecast skills especially for lower tropospheric humidity fields.

Kim, Min-Jeong↗
NASA NTRS2019

Developing Data Assimilated 4D Global Precipitation Products from the GEOS system in Support of the GPM Mission

This presentation is to report on the progresses made for the NASA Precipitation Measurement Mission (PMM) science team research tasks that are recently funded by NASA HQ. These tasks include (1) extending the existing GEOS all-sky analysis system to assimilate radiance data from other microwave sensors in GPM constellation satellites, (2) producing improved GEOS analyses of precipitation and downscaling of satellite precipitation estimates, and (3) providing the GEOS analyses to PMM Science team as prototype of GPM Level-4 products.

Kim, Min-Jeong↗
NASA NTRS2019

Impact of GMI All-Sky Radiance Assimilation in the NASA GEOS Forecast System

The assimilation of cloud- and precipitation-affected ("all-sky") radiances has become an important focus of development at most numerical weather prediction centers. Efforts at the Global Modeling and Assimilation Office (GMAO) have focused on all-sky assimilation of GPM Microwave Imager (GMI) radiances, which became operational in the GEOS real-time production system in July 2018. Implementation of the all-sky capability required several upgrades to the GEOS hybrid 4D-EnVar assimilation infrastructure including the addition of control variables for cloud liquid, cloud ice, rain and snow, enhancements to the radiative transfer model, new hybrid background and observational error models, and modified quality control and bias correction procedures. This talk describes the impact of GMI all-sky radiance assimilation on GEOS analyses and forecasts as determined from examination of various metrics including statistics of background departures and analysis increments, forecast skill scores, and forecast sensitivity observation impact (FSOI) calculations. It is shown that in addition to the hydrometeors themselves, the initial wind, temperature and pressure fields all undergo significant dynamic adjustment in response to the analyzed cloud and precipitation features. Assimilation of GMI radiances leads to improved forecasts of lower tropospheric wind, temperature and humidity, especially in the tropics. The largest forecast improvements occur during the first 48 hours, with diminishing impact thereafter. However, combining GMI all-sky assimilation with improvements to the GEOS model physics as in the recent implementation of the real-time production system, extends these forecast improvements well in to the medium range. FSOI results based on a 24-hr moist global energy norm show that GMI radiances provide nearly uniform beneficial impact throughout the tropics, with more mixed impacts in the subtropics. While the overall impact of GMI is smaller than that of other, much more numerous microwave and hyperspectral infrared radiance types, its impact is among the largest of all radiance types on a per-observation basis.

Gelaro, Ron↗
NASA NTRS2018

Assimilating GCOM-W AMSR2 Radiance Data in Future GEOS Reanalysis

Surface and atmospheric radiation are being measured by space-borne conical scanning microwave radiometers with high accuracy and spatial resolution since 1980s'. These radiometers include Special Sensor Microwave Imager (SSM/I) onboard various NOAA's polar-orbiting satellites, Special Sensor Microwave Imager Sounder (SSMIS) onboard Defense Meteorological Satellite Program (DMSP) satellites, and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI), and Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard NASA and JAXA's satellites. Atmospheric temperature, humidity, clouds, and precipitation are retrieved using brightness temperature data at frequencies between 22 GHz – 183 GHz. Some of the brightness temperature and retrieved rain rate data made by these imagers are assimilated in NASA Global Modelling and Assimilation Office (GMAO)'s weather and climate data sets including the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its version 2 (MERRA-2) data sets. However, those radiance data are assimilated with the Goddard Earth Observing System model, version 5 (GEOS-5) only in clear-sky conditions. Recently, we started assimilate GPM/GMI radiance data with GEOS-5's atmospheric data assimilation system GSI under all-sky conditions in order to have better constraints in analyzed hydrological properties. GMAO plans to assimilate more microwave radiometers' brightness temperature data in its analyses. In this talk, we will discuss TMI and AMSR2 data's impact in temperature, humidity, clouds and precipitation under all-sky conditions in future GEOS reanalysis.

Jin, Jianjun↗
NASA NTRS2018

Sensitivity of Different Types of Observations to NASA GEOS Hurricane Analyses and Forecasts

The 2017 Atlantic hurricane season was the 5th most active, featuring 17 named storms, the highest number of major hurricanes since 2005, and by far the costliest season on record. African easterly waves often serve as the seeding circulation for a large portion of hurricanes (i.e. tropical storms with wind over 74mph in the Atlantic and Northeast Pacific). Warm SST, moist air, and low wind shear are the main requirements for tropical cyclones to develop and maintain hurricane strength. In terms of hurricane propagation (so called hurricane tracks), Atlantic hurricanes typically propagate around the periphery of the subtropical ridge called the Bermuda High (Azores High), riding along its strongest winds. If the high is positioned to the east, then hurricanes generally propagate northeastward around the high's western edge into the open Atlantic Ocean without making land fall. If the high is positioned to the west and extends far enough to the south, storms are blocked from curving north and forced to continue west towards Florida, Cuba, and the Gulf of Mexico. If we have accurate atmospheric temperature distribution, which is directly related to atmospheric wave patterns, wind distributions, moisture distribution, and SST distribution in the analyses, we will have better NWP skills in hurricane analyses including hurricane intensity and tracks. Assimilating various observation data are supposed to play these roles in the analyses. To examine impacts of different types of observation data on NASA Goddard Earth Observing System (GEOS) model hurricane analyses and forecasts during the period of 2017 summer, this study performs data denial experiments using GEOS Atmospheric Data Assimilation System (ADAS), which is based on the hybrid 4D-EnVar GSI algorithm. Various types of observations such as microwave sounders, infrared sounders, TCvitals, and conventional data are removed in the experiments. In addition, the interaction between the different observation groups as certain instruments are removed from the analysis is investigated in detail using adjoint based forecast sensitivity observation impact (FSOI).

Hurricane↗
NASA NTRS2018

Examining Dense Data Usage near the Regions with Severe Storms in All-Sky Microwave Radiance Data Assimilation and Impacts on GEOS Hurricane Analyses

Many numerical weather prediction (NWP) centers assimilate radiances affected by clouds and precipitation from microwave sensors, with the expectation that these data can provide critical constraints on meteorological parameters in dynamically sensitive regions to make significant impacts on forecast accuracy for precipitation. The Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center assimilates all-sky microwave radiance data from various microwave sensors such as all-sky GPM Microwave Imager (GMI) radiance in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS), which includes the GEOS atmospheric model, the Gridpoint Statistical Interpolation (GSI) atmospheric analysis system, and the Goddard Aerosol Assimilation System (GAAS). So far, most of NWP centers apply same large data thinning distances, that are used in clear-sky radiance data to avoid correlated observation errors, to all-sky microwave radiance data. For example, NASA GMAO is applying 145 km thinning distances for most of satellite radiance data including microwave radiance data in which all-sky approach is implemented. Even with these coarse observation data usage in all-sky assimilation approach, noticeable positive impacts from all-sky microwave data on hurricane track forecasts were identified in GEOS-5 system. The motivation of this study is based on the dynamic thinning distance method developed in our all-sky framework to use of denser data in cloudy and precipitating regions due to relatively small spatial correlations of observation errors. To investigate the benefits of all-sky microwave radiance on hurricane forecasts, several hurricane cases selected between 2016-2017 are examined. The dynamic thinning distance method is utilized in our all-sky approach to understand the sources and mechanisms to explain the benefits of all-sky microwave radiance data from various microwave radiance sensors like Advanced Microwave Sounder Unit (AMSU-A), Microwave Humidity Sounder (MHS), and GMI on GEOS-5 analyses and forecasts of various hurricanes.

Kim, Min-Jeong↗
NASA NTRS2017

All-Sky Microwave Imager Data Assimilation at NASA GMAO

Efforts in all-sky satellite data assimilation at the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center have been focused on the development of GSI configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) and Global Change Observation Mission-Water (GCOM-W) Advanced Microwave Scanning Radiometer 2 (AMSR-2). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be relatively transparent to atmospheric gases and thin ice clouds, and highly sensitive to precipitation. Therefore, GMAOs all-sky data assimilation efforts are primarily focused on utilizing these data in precipitating regions. The all-sky framework being tested at GMAO employs the GSI in a hybrid 4D-EnVar configuration of the Goddard Earth Observing System (GEOS) data assimilation system, which will be included in the next formal update of GEOS. This article provides an overview of the development of all-sky radiance assimilation in GEOS, including some performance metrics. In addition, various projects underway at GMAO designed to enhance the all-sky implementation will be introduced.

NWP↗
NASA NTRS2015

Assimilation of Precipitation Measurement Missions Microwave Radiance Observations With GEOS-5

The Global Precipitation Mission (GPM) Core Observatory satellite was launched in February, 2014. The GPM Microwave Imager (GMI) is a conically scanning radiometer measuring 13 channels ranging from 10 to 183 GHz and sampling between 65 S 65 N. This instrument is a successor to the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI), which has observed 9 channels at frequencies ranging 10 to 85 GHz between 40 S 40 N since 1997. This presentation outlines the base procedures developed to assimilate GMI and TMI radiances in clear-sky conditions, including quality control methods, thinning decisions, and the estimation of, observation errors. This presentation also shows the impact of these observations when they are incorporated into the GEOS-5 atmospheric data assimilation system.

GPM/GMI↗
NASA NTRS2008

A Physical Model to Estimate Snowfall over Land using AMSU-B Observations

In this study, we present an improved physical model to retrieve snowfall rate over land using brightness temperature observations from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Microwave Sounder Unit-B (AMSU-B) at 89 GHz, 150 GHz, 183.3 +/- 1 GHz, 183.3 +/- 3 GHz, and 183.3 +/- 7 GHz. The retrieval model is applied to the New England blizzard of March 5, 2001 which deposited about 75 cm of snow over much of Vermont, New Hampshire, and northern New York. In this improved physical model, prior retrieval assumptions about snowflake shape, particle size distributions, environmental conditions, and optimization methodology have been updated. Here, single scattering parameters for snow particles are calculated with the Discrete-Dipole Approximation (DDA) method instead of assuming spherical shapes. Five different snow particle models (hexagonal columns, hexagonal plates, and three different kinds of aggregates) are considered. Snow particle size distributions are assumed to vary with air temperature and to follow aircraft measurements described by previous studies. Brightness temperatures at AMSU-B frequencies for the New England blizzard are calculated using these DDA calculated single scattering parameters and particle size distributions. The vertical profiles of pressure, temperature, relative humidity and hydrometeors are provided by MM5 model simulations. These profiles are treated as the a priori data base in the Bayesian retrieval algorithm. In algorithm applications to the blizzard data, calculated brightness temperatures associated with selected database profiles agree with AMSU-B observations to within about +/- 5 K at all five frequencies. Retrieved snowfall rates compare favorably with the near-concurrent National Weather Service (NWS) radar reflectivity measurements. The relationships between the NWS radar measured reflectivities Z(sub e) and retrieved snowfall rate R for a given snow particle model are derived by a histogram matching technique. All of these Z(sub e)-R relationships fall in the range of previously established Z(sub e)-R relationships for snowfall. This suggests that the current physical model developed in this study can reliably estimate the snowfall rate over land using the AMSU-B measured brightness temperatures.

Kim, Min-Jeong↗
NASA NTRS2007

Nonspherical and Spherical Characterization of Ice in Hurricane Erin for Wideband Passive Microwave Comparisons

In order to better understand the characteristics of frozen cloud particles in hurricane systems, computed brightness temperatures were compared with radiometric observations of Hurricane Erin (2001) from the NASA ER-2 aircraft. The focus was oil the frozen particle microphysics and the high frequencies (2 85 GHz) that are particularly sensitive to frozen particles. Frozen particles in hurricanes are an indicator of increasing hurricane intensity. In fact "hot towers" associated with increasing hurricane intensity are composed of frozen ice cloud particles. (They are called hot towers because their column of air is warmer than the surrounding air temperature, but above about 5-7 km to the tops of the towers at 15-19 km, the cloud particles are frozen.) This work showed that indeed, one can model information about cloud ice particle characteristics and indicated that nonspherical ice shapes, instead of spherical particles, provided the best match to the observations. Overall, this work shows that while non-spherical particles show promise, selecting and modeling a proper ice particle parameterization can be difficult and additional in situ measurements are needed to define and validate appropriate parameterizations. This work is important for developing Global Precipitation Measurement (GPM) mission satellite algorithms for the retrieval of ice characteristics both above the melting layer, as in Hurricane Erin, and for ice particles that reach the surface as falling snow.

Skofronick-Jackson, Gail↗
NASA NTRS2006

Percentage Contributions from Atmospheric and Surface Features to Computed Brightness Temperatures

Over the past few years, a few solid precipitation detection and retrieval algorithms have been developed and shown to be applicable for snowing clouds and blizzards. Current precipitating snow retrieval algorithms require the use of millimeter-wave radiometer observations. The millimeter-wave frequencies are especially sensitive to the scattering and emission properties of frozen particles due to the ice particle refractive index. These channels can also be used to discern information about the frozen particles above the melting layer. Passive radiometric channels respond to both the integrated particle mass throughout the volume and field of view, and to the amount, location, and size distribution of the frozen (and liquid) particles with the sensitivity varying for different frequencies and hydrometeor types. This work will show the percentage of the brightness temperature resulting from the liquid hydrometeor, frozen hydrometeor, relative humidity, and surface contributions. The focus will be on precipitating snow events and millimeter-wave frequencies however, other events and frequencies will be included in the analysis.

SkofronickJackson, Gail↗
NASA NTRS2005

Scattering Properties and Brightness Temperatures Associated with Solid Precipitation

In the past few years, early solid precipitation detection and retrieval algorithms have been developed and shown to be applicable for snowing clouds and blizzards. NOAA has an operational snow versus rain classifier based on AMSU-B observations. Solid precipitation retrieval algorithms reported in the literature over the past two years include those that rely on neural nets, statistics, or physical relationships. All of the algorithms require the use of millimeter-wave radiometer observations. The millimeter-wave frequencies are especially sensitive to the scattering and emission properties of frozen particles due to the ice particle refractive index. Passive radiometric channels respond to both the integrated particle mass throughout the volume and field of view, and to the amount, location, and size distribution of the frozen (and liquid) particles with the sensitivity varying for different frequencies and hydrometeor types. This investigation probes the sensitivity of scattering and absorption coefficients, and hence computed brightness temperatures, resulting from variations in solid precipitation cloud profiles. The first study compares the single scattering, absorption, and asymmetry parameters associated with snow particles in clouds. Several methodologies are used to convert the physical characteristics (e.g., shape, size distributions, ice-air-water ratios) of ice particles to electromagnetic properties (e.g., absorption, scattering, and asymmetry factors). These methodologies include: conversion to solid ice particles, homogeneous dielectric mixing, or discrete dipole approximation. Changes in the conversion methodology can produce computed brightness temperature differences greater than 50 Kelvin.

Skofronick-Jackson, Gail M.↗
NASA NTRS2005

Microwave Remote Sensing of Falling Snow

This study analyzes passive and active microwave measurements during the 2003 Wakasa Bay field experiment for understanding of the electromagnetic characteristics of frozen hydrometeors at millimeter-wave frequencies. Based on these understandings, parameterizations of the electromagnetic scattering properties of snow at millimeter-wave frequencies are developed and applied to the hydrometeor profiles obtained by airborne radar measurements. Calculated brightness temperatures and radar reflectivity are compared with the millimeter-wave measurements.

Kim, Min-Jeong↗