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NASA NTRS2014

Interannual Variation in Phytoplankton Class-specific Primary Production at a Global Scale

Phytoplankton is responsible for over half of the net primary production on earth. The knowledge on the contribution of various phytoplankton groups to the total primary production is still poorly understood. Data from satellite observations suggest that for upwelling regions, photosynthetic rates by microplankton is higher than that of nanoplankton but that when the spatial extent is considered, the production by nanoplankton is comparable or even larger than microplankton. Here, we used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of 4 phytoplankton groups to the total primary production. Globally, diatoms were the group that contributed the most to the total phytoplankton production (approx. 50%) followed by coccolithophores and chlorophytes. Primary production by diatoms was highest in high latitude (>45 deg) and in major upwelling systems (Equatorial Pacific and Benguela system). We assessed the effects of climate variability on the class-specific primary production using global (i.e. Multivariate El Nino Index, MEI) and 'regional' climate indices (e.g. Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO)). Most interannual variability occurred in the Equatorial Pacific and was associated with climate variability. These results provide a modeling and data assimilation perspective to phytoplankton partitioning of primary production and contribute to our understanding of the dynamics of the carbon cycle in the oceans at a global scale.

OBM↗

NASA NTRS2022

Future Climate Change Impact on Wildfire Danger Over the Mediterranean: the Case of Greece

Recent studies have shown that temperature and precipitation in the Mediterranean are expected to change, contributing to longer and more intense summer droughts that even extend out of season. In connection to this, the frequency of forest fire occurrence and intensity will likely increase. In the present study, the changes in future fire danger conditions are assessed for the different regions of Greece using the Canadian fire weather index (FWI). Gridded future climate output as estimated from three regional climate models from the Coordinated Regional Downscaling Experiment are utilized. We use three representative concentration pathways (RCPs) consisting of an optimistic emissions scenario where emissions peak and decline beyond 2020 (RCP2.6), a middle-of-the-road scenario (RCP4.5) and a pessimistic scenario, in terms of mitigation where emissions continue to rise throughout the century (RCP8.5). Based on established critical fire FWI threshold values for Greece, the future change in days with critical fire danger were calculated for different areas of Greece domains. The results show that fire danger is expected to progressively increase in the future especially in the high-end climate change scenario with southern and eastern regions of Greece expected to have up to 40 additional days of high fire danger relative to the late 20th century, on average. Crete, the Aegean Islands, the Attica region, as well as parts of Peloponnese are predicted to experience a stronger increase in fire danger.

Forest fires↗

NASA NTRS2014

ENSO Effect on East Asian Tropical Cyclone Landfall via Changes in Tracks and Genesis in a Statistical Model

Improvements on a statistical tropical cyclone (TC) track model in the western North Pacific Ocean are described. The goal of the model is to study the effect of El Nino-Southern Oscillation (ENSO) on East Asian TC landfall. The model is based on the International Best-Track Archive for Climate Stewardship (IBTrACS) database of TC observations for 1945-2007 and employs local regression of TC formation rates and track increments on the Nino-3.4 index and seasonally varying climate parameters. The main improvements are the inclusion of ENSO dependence in the track propagation and accounting for seasonality in both genesis and tracks. A comparison of simulations of the 1945-2007 period with observations concludes that the model updates improve the skill of this model in simulating TCs. Changes in TC genesis and tracks are analyzed separately and cumulatively in simulations of stationary extreme ENSO states. ENSO effects on regional (100-km scale) landfall are attributed to changes in genesis and tracks. The effect of ENSO on genesis is predominantly a shift in genesis location from the southeast in El Nino years to the northwest in La Nina years, resulting in higher landfall rates for the East Asian coast during La Nina. The effect of ENSO on track propagation varies seasonally and spatially. In the peak activity season (July-October), there are significant changes in mean tracks with ENSO. Landfall-rate changes from genesis- and track-ENSO effects in the Philippines cancel out, while coastal segments of Vietnam, China, the Korean Peninsula, and Japan show enhanced La Nina-year increases.

simulation↗

NASA NTRS2013

Impact of Radiatively Interactive Dust Aerosols in the NASA GEOS-5 Climate Model: Sensitivity to Dust Particle Shape and Refractive Index

We investigate the radiative effects of dust aerosols in the NASA GEOS-5 atmospheric general circulation model. GEOS-5 is improved with the inclusion of a sectional aerosol and cloud microphysics module, the Community Aerosol and Radiation Model for Atmospheres (CARMA). Into CARMA we introduce treatment of the dust and sea salt aerosol lifecycle, including sources, transport evolution, and sinks. The aerosols are radiatively coupled to GEOS-5, and we perform a series of multi-decade AMIP-style simulations in which dust optical properties (spectral refractive index and particle shape distribution) are varied. Optical properties assuming spherical dust particles are from Mie theory, while those for non-spherical shape distributions are drawn from a recently available database for tri-axial ellipsoids. The climatologies of the various simulations generally compare well to data from the MODIS, MISR, and CALIOP space-based sensors, the ground-based AERONET, and surface measurements of dust deposition and concentration. Focusing on the summertime Saharan dust cycle we show significant variability in our simulations resulting from different choices of dust optical properties. Atmospheric heating due to dust enhances surface winds over important Saharan dust sources, and we find a positive feedback where increased dust absorption leads to increased dust emissions. We further find that increased dust absorption leads to a strengthening of the summertime Hadley cell circulation, increasing dust lofting to higher altitudes and strengthening the African Easterly Jet. This leads to a longer atmospheric residence time, higher altitude, and generally more northward transport of dust in simulations with the most absorbing dust optical properties. We find that particle shape, although important for radiance simulations, is a minor effect compared to choices of refractive index, although total atmospheric forcing is enhanced by greater than 10 percent for simulations incorporating a spheroidal shape distribution versus ellipsoidal or spherical shapes.

radiative forcing↗

NASA NTRSDate not supplied

Detecting Short Term Drought Impact in the Southwest US Using GOES-16 ABI Data

Satellite optical remote sensing has been often used for monitoring broad-region vegetation change, for example, phenology observations and the year-to-year leaf area index (LAI) responses to climate oscillations. However, rapid responses of vegetation to day-to-day weather perturbations are difficult to detect using available optical remote sensing satellites because of the low frequency of the observations. Sun-synchronous optical sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR), can observe a target area once a day. Daily observations cannot distinguish whether short-term changes in Normalized Differential Vegetation Index (NDVI) are actual LAI change or cloud contamination. NOAA’s GOES satellites make observations every 10 to 15 minutes using the Advanced Baseline Imager (ABI). In this study, we used the Geostationary-NASA Earth Exchange (GeoNEX) L1G Top-of-Atmosphere (TOA) ABI data to detect drought impact on NDVI time series in the Southwest US. We used an empirical method to cancel the BRDF effect of varying solar zenith angle. The ABI was able to detect short term drought impacts as well as an NDVI decrease in the dry season. Increased NDVI right after a rainfall followed by an immediate decrease was observed. These ABI NDVI changes were correlated with the RGB time series from PhenoCam Network data. Results indicated that the ABI can be used for short-term analysis of LAI and can detect small LAI changes caused by drought in an arid area, suggesting the potential for its use in near-real time drought monitoring applications.

ABI↗

NASA NTRS2020

Forest Potential Productivity Mapping by Linking Remote-Sensing-Derived Metrics to Site Variables

A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, and topographic metrics to map improved BGI (iBGI) in parts of North American Acadian regions. Initially, several Sentinel-2 variables including nine single spectral bands and 12 spectral vegetation indices (SVIs) were used in combination with forest management variables to predict tree volume/ha and height using Random Forest. The results showed a 10–12 % increase in out of bag (OOB) r2when Sentinel-2 variables were included in the prediction of both volume and height together with BGI. Later, selected Sentinel-2 variables were used for biomass growth prediction in Maine, USA and New Brunswick, Canada using data from 7738 provincial permanent sample plots. The Sentinel-2 red-edge position (S2REP) index was identified as the most important variable over others to have known influence on site productivity. While a slight improvement in the iBGI accuracy occurred compared to the base BGI model (~2%), substantial changes to coefficients of other variables were evident and some site variables became less important when S2REP was included.

site productivity↗

NASA NTRS2013

Tree Line Structure and Dynamics at the Northern Limit of the Larch Forest: Anabar Plateau, Siberia, Russia

The goal of the study was to provide an analysis of climate impact before, during, and after the Little Ice Age (LIA) on the larch (Larix gmelinii) tree line at the northern extreme of Siberian forests. Recent decadal climate change impacts on the tree line, regeneration abundance, and age structure were analyzed. The location of the study area was within the forest-tundra ecotone (elevation range 170-450 m) in the Anabar Plateau, northern Siberia. Field studies were conducted along elevational transects. Tree natality/mortality and radial increment were determined based on dendrochronology analyses. Tree morphology, number of living and subfossil trees, regeneration abundance, and age structure were studied. Locations of pre-LIA, LIA, and post-LIA tree lines and refugia boundaries were established. Long-term climate variables and drought index were included in the analysis. It was found that tree mortality from the 16th century through the beginning of the 19th century caused a downward tree line recession. Sparse larch stands experienced deforestation, transforming into tundra with isolated relict trees. The maximum tree mortality and radial growth decrease were observed to have occurred at the beginning of 18th century. Now larch, at its northern boundary in Siberia, is migrating into tundra areas. Upward tree migration was induced by warming in the middle of the 19th century. Refugia played an important role in repopulation of the forest-tundra ecotone by providing a seed source and shelter for recruitment of larch regeneration. Currently this ecotone is being repopulated mainly by tree cohorts that were established after the 1930s. The last two decades of warming did not result in an acceleration of regeneration recruitment because of increased drought conditions. The regeneration line reached (but did not exceed) the pre-LIA tree line location, although contemporary tree heights and stand densities are comparatively lower than in the pre-LIA period. The mean rate of tree line upward migration has been about 0.35 m/yr (with a range of 0.21-0.58), which translates to a tree line response to temperature of about 55 m/deg C.

Northern Limit↗

NASA NTRS2023

Spatiotemporal Characterization of Droughts and Vegetation Response in Northwest Africa From 1981 to 2020

Drought has become one of the most devastating natural risks of agricultural production and the environment in almost all climate regions. Thus, understanding the spatiotemporal characteristics of drought and its associated impacts is crucial in drought early warning management and adaptation efforts. In this study, we used the 3-month Standardized Precipitation Index (SPI-3) obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) to investigate the space–time characteristics of drought conditions. Also, this study examined the impact of the SPI-based drought on vegetation health conditions using the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time-series data from 1981 to 2020. The results revealed that the region experiences drought primarily between July and September, with the most prolonged drought events lasting up to five months. Morocco suffered from more frequent droughts than other countries in the region. The Mann-Kendall test showed that the trend of drought became drier over the last decade, whereas the period from 1981 to 2010 witnessed either wetting or no trends. This study also found that the response of crops and grasslands showed higher correlation with the SPI-3 and that the response of vegetation to droughts was higher during the dry season. The findings of this study provide useful information to support local and regional drought planning and adaptation programs and enhance the understanding of drought development in the region.

Nguyen Quang Thi↗

NASA NTRS2001

A Time-Frequency Analysis of the Effects of Solar Activities on Tropospheric Thermodynamics

Whether the Sun has significantly influenced the climate during the last century has been under extensive debates for almost two decades. Since the solar irradiance varies very little in a solar cycle, it is puzzling that some geophysical parameters show proportionally large variations which appear to be responding to the solar cycles. For example, variation in low altitude clouds is shown correlated with solar cycle, and the onset of Forbush decrease is shown correlated with the reduction of the vorticity area index. A possible sun-climate connection is that galactic cosmic rays modulated by solar activities influence cloud formation. In this paper, we apply wavelet transform to satellite and surface data to examine this hypothesis. Data analyzed include the time series for solar irradiance, sunspots, UV index, temperature, cloud coverage, and neutron counter measurements. The interactions among the elements in the Earth System under the external and internal forcings give out very complex signals.The periodicity of the forcings or signals could range widely. Since wavelet transforms can analyze multi-scale phenomena that are both localized in frequency and time, it is a very useful technique for detecting, understanding and monitoring climate changes.

Kiang, Richard K.↗

NASA NTRS2013

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

Satellite observations of surface reflected solar radiation contain informationabout variability in the absorption of solar radiation by vegetation. Understanding thecauses of variability is important for models that use these data to drive land surface fluxesor for benchmarking prognostic vegetation models. Here we evaluated the interannualvariability in the new 30.5-year long global satellite-derived surface reflectance index data,Global Inventory Modeling and Mapping Studies normalized difference vegetation index(GIMMS NDVI3g). Pearsons correlation and multiple linear stepwise regression analyseswere applied to quantify the NDVI interannual variability driven by climate anomalies, andto evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVIsignal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systemswhere in some regions and seasons 40 of the NDVI variance could be explained byprecipitation anomalies. Temperature correlations were strongest in northern mid- to-high-latitudes in the spring and early summer where up to 70 of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America,winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wetseason precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

interference↗

NASA NTRS2001

Southern Ocean Climate and Sea Ice Anomalies Associated with the Southern Oscillation

The anomalies in the climate and sea ice cover of the Southern Ocean and their relationships with the Southern Oscillation (SO) are investigated using a 17-year of data set from 1982 through 1998. We correlate the polar climate anomalies with the Southern Oscillation index (SOI) and examine the composites of these anomalies under the positive (SOI > 0), neutral (0 > SOI > -1), and negative (SOI < -1) phases of SOL The climate data set consists of sea-level pressure, wind, surface air temperature, and sea surface temperature fields, while the sea ice data set describes its extent, concentration, motion, and surface temperature. The analysis depicts, for the first time, the spatial variability in the relationship of the above variables and the SOL The strongest correlation between the SOI and the polar climate anomalies are found in the Bellingshausen, Amundsen and Ross sea sectors. The composite fields reveal anomalies that are organized in distinct large-scale spatial patterns with opposing polarities at the two extremes of SOI, and suggest oscillating climate anomalies that are closely linked to the SO. Within these sectors, positive (negative) phases of the SOI are generally associated with lower (higher) sea-level pressure, cooler (warmer) surface air temperature, and cooler (warmer) sea surface temperature in these sectors. Associations between these climate anomalies and the behavior of the Antarctic sea ice cover are clearly evident. Recent anomalies in the sea ice cover that are apparently associated with the SOI include: the record decrease in the sea ice extent in the Bellingshausen Sea from mid- 1988 through early 199 1; the relationship between Ross Sea SST and ENSO signal, and reduced sea ice concentration in the Ross Sea; and, the shortening of the ice season in the eastern Ross Sea, Amundsen Sea, far western Weddell Sea, and the lengthening of the ice season in the western Ross Sea, Bellingshausen Sea and central Weddell Sea gyre over the period 1988-1994. Four ENSO episodes over the last 17 years contributed to a negative mean in the SOI (-0.5). In each of these episodes, significant retreats in the Bellingshausen/Amundsen Sea were observed providing direct confirmation of the impact of SO on the Antarctic sea ice cover.

Kwok, R.↗

NASA NTRS2014

Environmental Risk Factors and Child Nutritional Status and Survival in a Context of Climate Variability and Change

Much of the population in the developing world resides in rural areas, is dependent on local agriculture for survival, and thus directly subject to increasingly volatile and variable climatic patterns. Poverty limits options for adaptation to unpredictable weather and resultant food insecurity; these concerns are particularly salient in an era of climate change, which threatens to roll back years of development gains. Examining the association between the growing environment and child survival and nutrition is important in this context. Using NASA's satellite remote sensing data with Demographic and Health Surveys (DHS) data from four West African countries (Mali, Burkina Faso, Guinea and Benin), we assess the association between a climate-related environmental variable (vegetation index - NDVI) and child survival and nutrition. NDVI had a positive association with child survival and nutrition in countries with a wide distribution of NDVI values. NDVI was more likely to be positively associated with wasting rather than stunting. We find that environmental factors can be important for child survival and nutrition outcomes in specific contexts. Additional research is needed to further explore the ways NDVI can be used to inform our understanding of the environment's impact on child survival and nutrition.

Environment↗

NASA NTRSDate not supplied

Characterizing Wildfires in Western US.: A Cloud-based Case Study for Interdisciplinary Research using NASA Resources

This presentation will demonstrate a case study of interdisciplinary research done in the Amazon Web Services (AWS) cloud platform, in addition to in the local machine. We conduct data analysis next to data by leveraging various cloud-based data in NASA Earthdata Cloud, which are distributed by different missions/NASA Distributed Active Archive Centers (DAACs), and cloud computing resources at NASA. For instance, we directly access multiple datasets stored in the AWS Simple Storage Service (S3) buckets using a Python Jupyter notebook through a JupyterHub interface hosted in AWS (without having to download data), and conduct data analysis next to data in the cloud. We will also show how to share the research results following Open Source policy. This case study characterizes the change in wildfire events in the western United States during the past 20 years. In particular, we focus on the wildfires in California in 2021, one of the most severe wildfire years occurring in the most recent 20 years in California. We will analyze the possible causes of wildfires, such as drought conditions and climate variability, and examine the impacts of wildfires on air quality and atmospheric composition, and on land cover. We will examine the data distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), including aerosols and meteorological data from the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), precipitation from the Global Precipitation Measurement (GPM) and Global Precipitation Climate Project (GPCP), and aerosol index from Ozone Monitoring Instrument (OMI). We also utilize the data distributed by the Physical Oceanography (PO) DAAC, such as Sea Surface Temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST), and the data distributed by Land Processes (LP) DAAC, such as Normalized Difference Vegetation Index (NDVI).

Xiaohua Pan↗

NASA NTRS2019

A Landslide Climate Indicator from Machine Learning

In order to create a Landslide Hazard Index, we accessed rain, snow, and a dozen other variables from the National Climate Assessment Land Data Assimilation System. These predictors were converted to probabilities of landslide occurrence with XGBoost, a major machine-learning tool. The model was fitted with thousands of historical landslides from the Pacific Northwest Landslide Inventory (PNLI).

Stanley, T. A.↗

NASA NTRS2017

Satellite-Based Drought Reporting on the Navajo Nation

The Navajo Nation (NN) is the largest reservation in the US, and faces challenges related to water management during long-term and widespread drought episodes. The Navajo Nation is a federally recognized tribe, which has boundaries within Arizona, New Mexico, and Utah. The Navajo Nation has a land area of over 70,000 square kilometers. The Navajo Nation Department of Water Resources (NNDWR) reports on drought and climatic conditions through the use of regional Standardized Precipitation Index (SPI) values and a network of in-situ rainfall, streamflow, and climate data. However, these data sources lack the spatial detail and consistent measurements needed to provide a coherent understanding of the drought regime within the Nation's regional boundaries. This project, as part of NASA's Western Water Applications Office (WWAO), improves upon the recently developed Drought Severity Assessment Tool (DSAT) to ingest satellite-based precipitation data to generate SPI values for specific administrative boundaries within the reservation. The tool aims to: (1) generate SPI values and summary statistics for regions of interest on various timescales, (2) to visualize SPI values within a web-map application, and (3) produce maps and comparative statistical outputs in the format required for annual drought reporting. The co-development of the DSAT with NN partners is integral to increasing the sustained use of Earth Observations for water management applications. This tool will provide data to support the NN in allocation of drought contingency dollars to the regions most adversely impacted by declines in water availability.

Satellite-based↗

NASA NTRS2008

Effective Interpolation of Incomplete Satellite-Derived Leaf-Area Index Time Series for the Continental United States

Many earth science modeling applications employ continuous input data fields derived from satellite data. Environmental factors, sensor limitations and algorithmic constraints lead to data products of inherently variable quality. This necessitates interpolation of one form or another in order to produce high quality input fields free of missing data. The present research tests several interpolation techniques as applied to satellite-derived leaf area index, an important quantity in many global climate and ecological models. The study evaluates and applies a variety of interpolation techniques for the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf-Area Index Product over the time period 2001-2006 for a region containing the conterminous United States. Results indicate that the accuracy of an individual interpolation technique depends upon the underlying land cover. Spatial interpolation provides better results in forested areas, while temporal interpolation performs more effectively over non-forest cover types. Combination of spatial and temporal approaches offers superior interpolative capabilities to any single method, and in fact, generation of continuous data fields requires a hybrid approach such as this.

Jasinski, Michael F.↗

NASA NTRS2016

Tropical Cyclone Activity in the North Atlantic Basin During the Weather Satellite Era, 1960-2014

This Technical Publication (TP) represents an extension of previous work concerning the tropical cyclone activity in the North Atlantic basin during the weather satellite era, 1960-2014, in particular, that of an article published in The Journal of the Alabama Academy of Science. With the launch of the TIROS-1 polar-orbiting satellite in April 1960, a new era of global weather observation and monitoring began. Prior to this, the conditions of the North Atlantic basin were determined only from ship reports, island reports, and long-range aircraft reconnaissance. Consequently, storms that formed far from land, away from shipping lanes, and beyond the reach of aircraft possibly could be missed altogether, thereby leading to an underestimate of the true number of tropical cyclones forming in the basin. Additionally, new analysis techniques have come into use which sometimes has led to the inclusion of one or more storms at the end of a nominal hurricane season that otherwise would not have been included. In this TP, examined are the yearly (or seasonal) and 10-year moving average (10-year moving average) values of the (1) first storm day (FSD), last storm day (LSD), and length of season (LOS); (2) frequencies of tropical cyclones (by class); (3) average peak 1-minute sustained wind speed ( ) and average lowest pressure ( ); (4) average genesis location in terms of north latitudinal ( ) and west longitudinal ( ) positions; (5) sum and average power dissipation index ( ); (6) sum and average accumulated cyclone energy ( ); (7) sum and average number of storm days ( ); (8) sum of the number of hurricane days (NHD) and number of major hurricane days (NMHD); (9) net tropical cyclone activity index (NTCA); (10) largest individual storm (LIS) PWS, LP, PDI, ACE, NSD, NHD, NMHD; and (11) number of category 4 and 5 hurricanes (N4/5). Also examined are the December-May (D-M) and June-November (J-N) averages and 10-year moving average values of several climatic factors, including the (1) oceanic Nino index ( ); (2) Atlantic multi-decadal oscillation ( ) index; (3) Atlantic meridional mode ( ) index; (4) global land-ocean temperature index ( ); and (5) quasi-biennial oscillation ( ) index. Lastly, the associational aspects (using both linear and nonparametric statistical tests) between selected tropical cyclone parameters and the climatic factors are examined based on their 10-year moving average trend values.

satellite↗

NASA NTRS2008

Relationships Between TRMM Precipitation and Upper-Tropospheric Hydrometeors as Seen From AMSU-B/MHS and A-Train Sensors

Tropical rainfall as seen by the TRMM radar has multiple scales of organization, one prominent example of which is mesoscale deep convection that supports the production of strong, widespread anvil systems important to the planet's water and energy balance. TRMM PR precipitation retrievals (i.e. the 2A25 algorithm) are reliable down to rates below 1.0 mm/h which captures the majority of near-surface rainfall. However, much of the precipitating hydrometeor mass above the freezing level in these anvil systems may be associated with particles where TRMM PR s/n is low. In our analysis we are examining the question of "What portions of the total hydrometeor spectrum can we see individually with TRMM, CloudSat, high frequency passive microwave (e.g. AMSU-B, MHS) and MODIS". This will allow us to pursue fundamental issues of precipitation efficiency, maintenance of upper-tropospheric humidity, and cloud forcing variability in the tropical climate system. We do this by generating frequency distributions of ice water content (IWC), integrated IWC (IWP), and precipitation as appropriate for these sensors and relate these to TRMM near-surface rainfall. Joint frequency distributions are developed from more limited coincidence between TRMM and these sensors. We interpret these results in terms of a climate regime descriptor and as an index of precipitation efficiency for tropical rain systems.

Robertson, Franklin↗