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NASA NTRSDate not supplied

The ACCP Inclined Orbit Project: Enabling New and Synergistic Aerosol Remote Sensing Inversions

Atmospheric aerosols have high spatiotemporal variations due to their diverse sources including windblown dust, wildfires, volcanic eruptions, and anthropogenic emissions. These aerosols also have strong diurnal emission characteristics that influence long-range transport, air quality, Earth’s radiation budget, biogeochemical cycles, and weather forecasting. While current sensors can measure the steady-state of global 3D cloud and aerosol properties, we lack the observational datasets to discover the diurnal variability of aerosols, even at seasonal and regional scales, because these missions utilize sun synchronous orbits. An Aerosols and Clouds, Convection and Precipitation (ACCP) observing system has been established as part of NASA’s new Earth System Observatory (ESO) to fulfill the science needs presented in the 2017 Earth Science Decadal Survey. The selected ACCP architecture encompasses sensors in two orbit planes, one of which utilizes an inclined orbit (nominally ~55 degrees at a 407 km altitude) to advance our understanding of aerosol and cloud properties and target the dynamics of the cloud and aerosol processes on sub-daily time scales. The anticipated instrumentation suite for the ACCP Inclined Orbit Project is a Stereo Camera System, Polarimeter, Microwave Radiometer, Two-Frequency Radar, and Backscatter Lidar. The polarimeter is expected to have multiple wavelength bands and viewing angles to enable accurate retrievals of aerosol microphysical and optical properties. The backscatter lidar will provide vertical profiles of attenuated backscatter and depolarization ratio at two wavelengths, with better daytime signal-to-noise ratio than previous space-based lidar systems to improve aerosol detection/typing and estimates of aerosol optical properties. The stereo camera system, while designed primarily for clouds, will also provide aerosol plume heights and air motions at the plume heights. This invited presentation will provide (1) an overview of the ACCP Inclined Orbit Project, (2) a description of the new sensor capabilities and orbital characteristics, and (3) a preview of the potential for new synergistic aerosol inversion techniques.

John E Yorks↗
NASA NTRS2014

Global Precipitation Measurement (GPM) Mission Products and Services at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

On February 27, 2014, the NASA Global Precipitation Measurement (GPM) mission was launched to provide the next-generation global observations of rain and snow (http:pmm.nasa.govGPM). The GPM mission consists of an international network of satellites in which a GPM Core Observatory satellite carries both active and passive microwave instruments to measure precipitation and serve as a reference standard, to unify precipitation measurements from a constellation of other research and operational satellites. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) hosts and distributes GPM data within the NASA Earth Observation System Data Information System (EOSDIS). The GES DISC is home to the data archive for the GPM predecessor, the Tropical Rainfall Measuring Mission (TRMM). Over the past 16 years, the GES DISC has served the scientific as well as other communities with TRMM data and user-friendly services. During the GPM era, the GES DISC will continue to provide user-friendly data services and customer support to users around the world. GPM products currently and to-be available include the following:Level-1 GPM Microwave Imager (GMI) and partner radiometer productsLevel-2 Goddard Profiling Algorithm (GPROF) GMI and partner productsLevel-3 daily and monthly productsIntegrated Multi-satellitE Retrievals for GPM (IMERG) products (early, late, and final) A dedicated Web portal (including user guides, etc.) has been developed for GPM data (http:disc.sci.gsfc.nasa.govgpm). Data services that are currently and to-be available include Google-like Mirador (http:mirador.gsfc.nasa.gov) for data search and access; data access through various Web services (e.g., OPeNDAP, GDS, WMS, WCS); conversion into various formats (e.g., netCDF, HDF, KML (for Google Earth), ASCII); exploration, visualization, and statistical online analysis through Giovanni (http:giovanni.gsfc.nasa.gov); generation of value-added products; parameter and spatial subsetting; time aggregation; regridding; data version control and provenance; documentation; science support for proper data usage, FAQ, help desk; monitoring services (e.g. Current Conditions) for applications.

precipitation↗
NASA NTRS2000

Water Vapor Transport Over the Tropical Oceans During ENSO as Diagnosed from TRMM and SSM/I Data

Traditionally, large-scale water vapor transport [div Q] has been derived directly from circulation statistics in which transport processes are often depicted by mean and eddy motions. Thus detailed and accurate calculations of moisture transport terms over the globe are required. Notably, the lack of systematically spaced conventional measurements of meteorological variables over oceans has hindered understanding of the distribution and transport of water vapor. This motivates the use of indirect calculation methods in which horizontal divergence of water vapor is balanced by the evaporation minus precipitation, assuming the rate of changes of precipitable water and condensates is small over a sufficiently long time period. In order to obtain the water vapor transport, we need evaporation rate minus precipitation (E-P). Focussing on the differences in water vapor transport between El Nino and La Nina periods and their influences on atmospheric circulations, we study January, February, and March of 1998 and 1999 periods which represent El Nino and La Nina respectively. SSM/I-derived precipitation and evaporation rate from SSM/I wind and total precipitable water, in conjunction with NCEP SST and surface air temperature, are used for the calculation of the transport potential function. For the retrieval of evaporation we use a stability-dependent aerodynamic bulk scheme developed by Chou (1993). It was tested against aircraft covariance fluxes measured during cold air outbreaks over the North Atlantic Ocean. Chou et al. (1997) reported that the SSM/I retrieved latent heat flux over the western Pacific warm pool area were found to be comparable with daily mean fluxes of a ship measurements during TOGA/COARE.

Robertson, Franklin R.↗
NASA NTRS2010

Use of Quality Controlled AIRS Temperature Soundings to Improve Forecast Skill

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. Also included are the clear column radiances used to derive these products which are representative of the radiances AIRS would have seen if there were no clouds in the field of view. All products also have error estimates. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an rms error of 1K, and layer precipitable water with an rms error of 20 percent, in cases with up to 90 percent effective cloud cover. The products are designed for data assimilation purposes for the improvement of numerical weather prediction, as well as for the study of climate and meteorological processes. With regard to data assimilation, one can use either the products themselves or the clear column radiances from which the products were derived. The AIRS Version 5 retrieval algorithm is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates for retrieved quantities and clear column radiances, and the use of these error estimates for Quality Control. The temperature profile error estimates are used to determine a case-by-case characteristic pressure pbest, down to which the profile is considered acceptable for data assimilation purposes. The characteristic pressure p(sub best) is determined by comparing the case dependent error estimate (delta)T(p) to the threshold values (Delta)T(p). The AIRS Version 5 data set provides error estimates of T(p) at all levels, and also profile dependent values of pbest based on use of a Standard profile dependent threshold (Delta)T(p). These Standard thresholds were designed as a compromise between optimal use for data assimilation purposes, which requires highest accuracy (tighter Quality Control), and climate purposes, which requires more spatial coverage (looser Quality Control). Subsequent research using Version 5 sounding and error estimates showed that tighter Quality Control performs better for data assimilation proposes, while looser Quality Control better spatial coverage) performs better for climate purposes. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 degree latitude x 0.67 degree longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates (delta)T(p) were used as the uncertainty for each measurement in the data assimilation process.

Susskind, Joel↗
NASA NTRS2016

Transitioning Enhanced Land Surface Initialization and Model Verification Capabilities to the Kenya Meteorological Department (KMD)

Flooding, severe weather, and drought are key forecasting challenges for the Kenya Meteorological Department (KMD), based in Nairobi, Kenya. Atmospheric processes leading to convection, excessive precipitation and/or prolonged drought can be strongly influenced by land cover, vegetation, and soil moisture content, especially during anomalous conditions and dry/wet seasonal transitions. It is thus important to represent accurately land surface state variables (green vegetation fraction, soil moisture, and soil temperature) in Numerical Weather Prediction (NWP) models. The NASA SERVIR and the Short-term Prediction Research and Transition (SPoRT) programs in Huntsville, AL have established a working partnership with KMD to enhance its regional modeling capabilities. SPoRT and SERVIR are providing experimental land surface initialization datasets and model verification capabilities for capacity building at KMD. To support its forecasting operations, KMD is running experimental configurations of the Weather Research and Forecasting (WRF; Skamarock et al. 2008) model on a 12-km/4-km nested regional domain over eastern Africa, incorporating the land surface datasets provided by NASA SPoRT and SERVIR. SPoRT, SERVIR, and KMD participated in two training sessions in March 2014 and June 2015 to foster the collaboration and use of unique land surface datasets and model verification capabilities. Enhanced regional modeling capabilities have the potential to improve guidance in support of daily operations and high-impact weather and climate outlooks over Eastern Africa. For enhanced land-surface initialization, the NASA Land Information System (LIS) is run over Eastern Africa at ~3-km resolution, providing real-time land surface initialization data in place of interpolated global model soil moisture and temperature data available at coarser resolutions. Additionally, real-time green vegetation fraction (GVF) composites from the Suomi-NPP VIIRS instrument is being incorporated into the KMD-WRF runs, using the product generated by NOAA/NESDIS. Model verification capabilities are also being transitioned to KMD using NCAR's Model *Corresponding author address: Jonathan Case, ENSCO, Inc., 320 Sparkman Dr., Room 3008, Huntsville, AL, 35805. Email: Jonathan.Case-1@nasa.gov Evaluation Tools (MET; Brown et al. 2009) software in conjunction with a SPoRT-developed scripting package, in order to quantify and compare errors in simulated temperature, moisture and precipitation in the experimental WRF model simulations. This extended abstract and accompanying presentation summarizes the efforts and training done to date to support this unique regional modeling initiative at KMD. To honor the memory of Dr. Peter J. Lamb and his extensive efforts in bolstering weather and climate science and capacity-building in Africa, we offer this contribution to the special Peter J. Lamb symposium. The remainder of this extended abstract is organized as follows. The collaborating international organizations involved in the project are presented in Section 2. Background information on the unique land surface input datasets is presented in Section 3. The hands-on training sessions from March 2014 and June 2015 are described in Section 4. Sample experimental WRF output and verification from the June 2015 training are given in Section 5. A summary is given in Section 6, followed by Acknowledgements and References.

land surface modeling↗
NASA NTRS1993

An intercomparison of three satellite infrared rainfall techniques over Japan and surrounding waters

Three satellite infrared techniques of increasing complexity for the estimation of rainfall are compared: (1) the GOES precipitation index (Arkin, 1979, 1983), (2) the Negri-Adler-Wetzel (Negri et al., 1984) technique, and (3) the convective-stratiform technique (CST), described by Adler and Negri (1988). It is shown that all three techniques performed poorly in estimating the rain maxima over southeastern Japan associated with shallow orographic warm rain systems. However, when the data set was limited to the ocean-only points, the CST method performed best, with the lowest bias, the lowest rms, and the highest correlation. Daily rainfall estimates had rms errors of almost 200 percent of the mean, and negative biases of about 50 percent of the mean.

Negri, Andrew J.↗
NASA NTRS2016

Evaluation of the Sensitivity of the Amazonian Diurnal Cycle to Convective Intensity in Reanalyses

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics-specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Itterly, Kyle F.↗
NASA NTRS2000

Sensing Water Vapon via Spacecraft Radio Occultation Observations

The radio occultation technique has been used to characterize planetary atmospheres since the 1960's spanning atmospheric pressures from 16 microbars to several bars. In 1988, the use of GPS signals to make occultation observations of Earth's atmosphere was realized by Tom Yunck and Gunnar Lindal at JPL. In the GPS to low-Earth-orbiter limb- viewing occultation geometry, Fresnel diffraction yield a unique combination of high vertical resolution of 100 m to 1 km at long wavelengths (approx. 20 cm) insensitive to particulate scattering which allows routine limb sounding from the lower mesosphere through the troposphere. A single orbiting GPS/GLONASS receiver can observe - 1000 to 1400 daily occultations providing as many daily, high vertical resolution soundings as the present global radiosonde network, but with far more evenly distributed, global coverage. The occultations yield profiles of refractivity as a function of height. In the cold, dry conditions of the upper troposphere and above (T less than 240 K), profiles of density, pressure (geopotential), and temperature can be derived. Given additional temperature information, water vapor can be derived in the midddle and lower troposphere with a unique combination of vertical resolution, global distribution and insensitivity to clouds and precipitation to an accuracy of approx. 0.2 g/kg. At low latitudes, moisture profiles will be accurate to 1-5% within the convective boundary layer and better than 20% below 6 to 7 km. Accuracies of climatological averages should be approx. 0. 1 g/kg limited by the biases in the temperature estimates. To use refractivity to constrain water vapor, knowledge of temperature is required. The simplest approach is to use the temperature field from an analysis such as the 6 hour ECMWF global analysis interpolated to the locations of each occultation. A better approach is to combine the temperature and moisture fields from such an analysis with the occultation refractivity in a weighting scheme based on the errors in each data field. A ID variational combinational approach has been developed at the UKMO. We win present results from both approaches from GPS/MET data taken in June and July 1995 and compare them with the ECMWF global 6 hour moisture analyses which are derived largely from TOVS and radiosonde data. Overall, the atmosphere below the 500 mb level appears somewhat drier in general than the ECNIWF humidity field. A 2-D (latitude vs. height) climatological snapshot derived from a 2-week span of GPS/MET data will be compared to the humidity climatology of Peixoto and Oort derived from radiosonde data from 1963-1973. Differences between the GPS results and Peixoto and Oort may be the signature of a climate trend over the past 30 years.

Kursinski, E. Robert↗
NASA NTRS1996

Characterization and Evaluation of Acid Rain in East Central Florida from 1978 to 1995 and Evaluation of Some Chromatographic/Spectroscopic Results from Leachate Samples from CELSS

The results of monitoring the chemical composition of rain in east-central Florida have shown that the rain is moderately acid. The measured acidity of rain is less than that observed in other regions of the U. S., however, it does suggest that the level of acidity is substantial. The annual chemical composition of rain at UCF and at KSC has shown moderate variability. Extreme daily and monthly variations are observed, however these variations are not addressed here. The total ionic composition of rain collected at FL99 is greater than that for rain collected at UCF, however this can be accounted for by site proximity to the ocean with the accompanying marine influence. Difference in acidity data collected from the UCF and FL99 sites which are separated by 50 km may be due in part to the differences that have been observed between laboratory and field pH measurements. Trend assessment for precipitation composition requires evaluation of data that covers some minimum time period. In fact, the subdivision of the multi-year UCF record into individual 10 year records as described above can lead to the conclusion that a significant increase, a significant decrease or no trend exists for acidity depending upon the time period chosen for evaluation. Trend evaluation has also been accomplished by linear and nonlinear regression analysis using monthly volume weighted average concentrations and deposition using the UCF data set and some of the Florida NADP data set.

Madsen, Brooks C.↗
NASA NTRS1977

ATS-6 ascending - Near horizon measurements over water at 30 GHz

The return of the ATS-6 satellite to a western longitude during the fall of 1976 presented a unique opportunity to perform low-angle-of-elevation measurements at 30 GHz. For this purpose a receiver using a 1.5 m antenna was set up at Port Aransas, Texas, resulting in a propagation path entirely over water. The 30 GHz beacon was monitored daily for at least one hour from 8 September to 21 September 1976. During the time the elevation angle changed from 1.5 to 17.3 deg, the mean attenuation decreased from 20 to 2 dB and the standard deviation from over 6 dB to less than .2 dB. The deep fades at angles below 4 deg show significantly sharper nulls than peaks on a log scale. Spectra of the log amplitude fluctuations vary as the (-8/3) power of the spectral frequency in the limit. A flattening is noticeable at the low frequencies. A precipitation even at 8.5 deg elevation produced a 16 dB fade and significantly increased the variance.

Vogel, W. J.↗
NASA NTRS1977

ATS-6 ascending: Near horizon measurements over water at 30 GHz

The return of the ATS 6 satellite to a western longitude during the fall of 1976 presented a unique opportunity to perform low angle of elevation measurements at 30 GHz. For this purpose a receiver using a 1.5 m antenna was set up at Port Aransas, Texas, resulting in a propagation path entirely over water. The 30 GHz beacon was monitored daily for at least one hour from 8 September 1976 to 21 September 1976. During the time the elevation angle changed from 1.5 deg to 17.3 deg, the mean attenuation decreased from 20 dB to 2 dB and the standard deviation from over 6 dB to less than .2 dB. The deep fades at angles below 4 deg show significantly sharper nulls than peaks on a log scale. Spectra of the log amplitude fluctuations vary as the (-8/3) power of the spectral frequency in the limit. A flattening is noticeable at the low frequencies. A precipitation event at 8.5 deg elevation produced a 16 dB fade and significantly increased the variance.

Vogel, W. J.↗
NASA NTRS2014

Interannual Variations and Trends in Global Land Surface Phenology Derived from Enhanced Vegetation Index During 1982-2010

Land swiace phenology is widely retrieved from satellite observations at regional and global scales, and its long-term record has been demonstmted to be a valuable tool for reconstructing past climate variations, monitoring the dynamics of terrestrial ecosystems in response to climate impacts, and predicting biological responses to future climate scenarios. This srudy detected global land surface phenology from the advanced very high resolution radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 1982 to 2010. Based on daily enhanced vegetation index at a spatial resolution of 0.05 degrees, we simulated the seasonal vegetative trajectory for each individual pixel using piecewise logistic models, which was then used to detect the onset of greenness increase (OGI) and the length of vegetation growing season (GSL). Further, both overall interannual variations and pixel-based trends were examIned across Koeppen's climate regions for the periods of 1982-1999 and 2000-2010, respectively. The results show that OGI and OSL varied considerably during 1982-2010 across the globe. Generally, the interarmual variation could be more than a month in precipitation-controlled tropical and dry climates while it was mainly less than 15 days in temperature-controlled temperate, cold, and polar climates. OGI, overall, shifted early, and GSL was prolonged from 1982 to 2010 in most climate regions in North America and Asia while the consistently significant trends only occurred in cold climate and polar climate in North America. The overall trends in Europe were generally insignificant. Over South America, late OGI was consistent (particularly from 1982 to 1999) while either positive or negative OSL trends in a climate region were mostly reversed between the periods of 1982-1999 and 2000-2010. In the Northern Hemisphere of Africa, OGI trends were mostly insignificant, but prolonged GSL was evident over individual climate regions during the last 3 decades. OGI mainly showed late trends in the Southern Hemisphere of Africa while GSL was reversed from reduced GSL trends (1982-1999) to prolonged trends (2000-2010). In Australia, GSL exhibited considerable interannual variation, but the consistent trend lacked presence in most regions. Finally, the proportion of pixels with significant trends was less than I% in most of climate regions although it could be as large as 10%.

remote sensing↗
NASA NTRS2021

The Analysis of Using Satellite Soil Moisture Observations for Flood Detection, Evaluating Over the Thailand’s Great Flood of 2011

A flood monitoring and warning system provides critical information that can protect property and save lives. A basin-scale flood monitoring system requires an effective observation platform that offers extensive ground coverage of flood conditions, low latency, and high spatiotemporal resolution. While satellite imagery offers substantial spatial flood extent in detail due to its high spatial resolution, the coarse temporal resolution and cloud obstruction limit its near real-time application. Daily soil moisture data derived from satellite sensors at a scale of a few km can be used to monitor extreme wet surface conditions arising in flood occurrences. This study analyses the flood detection capabilities of several sources of soil moisture information, including the Soil Moisture and Ocean Salinity mission (SMOS), the Advanced Microwave Scanning Radiometer on EOS (AMSR-E), the Advanced SCATterometer (ASCAT) on MetOp, the Global Land Data Assimilation System (GLDAS), and the WaterGAP Global Hydrology Model (WGHM). In addition to soil moisture, the analysis includes measurements of surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation measurements from the Tropical Rainfall Measuring Mission (TRMM), and terrestrial water storage estimates from the Gravity Recovery And Climate Experiment (GRACE) as proxies for flood inundations. The analysis was conducted over the Chao Phraya River Basin (CPB) in Thailand, where the Great Flood of 2011 led to one of the most significant economic losses in the country's history. Satellite-derived soil moisture exhibits a stronger correlation with the flood inundations than the precipitation, model-derived soil moisture, and terrestrial water storage data. SMOS soil moisture observation agrees best with the MODIS-derived flood extent/occurrence, both in terms of spatial distribution and timing, and providing approximated flood lead-time of one week or longer. A neural network constructed from SMOS and MODIS data is used to predict flood intensity/occurrence (given soil moisture input) with a predicted time window from eight days to thirty-two days. The short-term prediction (e.g., eight days) achieves the highest accuracy with an averaged recovery rate of approximately 60% (correlation coefficient). This study's results suggest a potential application of satellite soil moisture data in assisting flood monitoring and warning systems.

Satellite soil moisture↗
NASA NTRS2004

Warm Rain Processes Over the Tropical Oceans and Implications on Climate Change

In this talk, we will first show results from TRMM regarding the characteristics of warm rains over the tropical oceans, and the dependence of rate of warm rain production on sea surface temperature. Results lead to the hypothesis that warm rain production efficiency, i.e., autoconversion, may be increased in a warm climate. We use the GEOS-II GCM to test this hypothesis. Our modeling results show that in a climate with increased rate of autoconversion, the total rain amount is increased, with warm rain contributing to a larger portion of the increase. The abundant rainout of warm precipitation at middle to low levels causes a reduction of high cloud cover due to the depletion of water available for ice-phase rain production. As a result, more isolated, but more intense penetrative convection develops. Results also show that increased autoconversion reduces the convective adjustment time scale tends, implying a faster recycling of atmospheric water. Most interestingly, the increased low level heating associated with warm rain leads to more energetic Madden and Julian oscillations in the tropics, with well-defined eastward propagation. While reducing the autoconversion leads to an abundant mix of westward and eastward tropical disturbance on daily to weekly time scales. The causes of the sensitivity of the dynamical regimes to the microphysics parameterization in the GCM will be discussed.

Lau, William K. M.↗
NASA NTRS2000

MM-Wave Radiometric Measurements of Low Amounts of Precipitable Water Vapor

An experiment was conducted during March, 1999 to study ways in which to improve techniques for measuring low amounts of total-column precipitable water vapor (PWV). The experiment was conducted at the DOE's ARM program's North Slope of Alaska/Adjacent Arctic Ocean Cloud and Radiation Testbed site (DoE ARM NSA/AAO CaRT) located just outside Barrow, Alaska. NASA and NOAA deployed a suite of radiometers covering 25 channels in the frequency range of 20 GHz up to 340 GHz including 8 channels around the 183 GHz water vapor absorption line. In addition to the usual CaRT site instrumentation the NOAA Depolarization and Backscatter Unattended Lidar (DABUL), the SUNY Rotating Shadowband Spectroradiometer (RSS) and other surface based meteorological instrumentation were deployed during the intensive observation period. Vaisala RS80 radiosondes were launched daily as well as nearby National Weather Service VIZ sondes. Atmospheric conditions ranged from clear calm skies to blowing snow and heavy multi-layer cloud coverage. Measurements made by the radiosondes indicate the PWV varied from approx. 1 to approx. 5 mm during the experiment. The near-surface temperature varied between about -40 C to - 15 C. In this presentation, an overview of the experiment with examples of data collected will be presented. Application of the data for assessing the potential and limitations of millimeter-wave radiometry for retrieving very low amounts of PWV will be discussed.

Racette, P.↗
NASA NTRSDate not supplied

On the Prediction of Surface Melt Over the Greenland Ice Sheet at NWP and S2S Timescales

Greenland surface melt “events” – encompassing large swaths of the ice sheet over approximately 4-10 day periods – have occurred with increasing frequency in the 21st Century. Surface processes are significant to the ice sheet’s overall contribution to sea level. In addition, the sudden onset of widespread surface melt produces hazardous conditions for researchers and for communities adjacent to the ice sheet. The mechanisms producing melt events, their associated large-scale forcing, and timescales of interest are highly variable and poorly understood. Here we examine the predictability of melt events in a numerical weather prediction system (NWP) and a subseasonal-to-seasonal system (S2S). The NASA Global Modeling and Assimilation Office forward processing (GMAO-FP) is an NWP system that incorporates an evolving, global hybrid 4D ensemble–variational (4DEnVar) data assimilation system and a state-of-the-art model that is run twice-daily with forecasts out to five and ten days. The GMAO S2S system is a hindcast-anomaly, coupled-model prediction system initialized with an atmospheric analysis, and in situ and satellite ocean data assimilation. Both systems represent ice sheet surface hydrology and snowpack processes. We examine the performance of the systems over the period 2018-2023, a span that includes approximately nine significant events, against analyses and satellite-derived melt extent. In addition to extent, we examine related conditions and their role in the melt forecast, including the onset of both omega and Rex atmospheric blocking patterns, sea surface temperatures, sea ice extent, water vapor transport, precipitation, and surface preconditioning. We further examine the tendency for false-alarm events in each system and assess reasons for the forecast performance. North Atlantic atmospheric blocking patterns leading to surface melt are well forecast by the NWP system out to around eight days, while the S2S system generally underestimates the magnitude of such events at monthly lead times. These systems suggest that while short term forecasts of ice sheet melt and melt events are relatively robust, S2S predictions of surface conditions are highly dependent on the ability of a coupled system to generate the short-term atmospheric conditions associated with surface melt events.

Richard Cullather↗
NASA NTRS2021

Data-driven landslide nowcasting at the global scale

Landslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60° North-South every 3 h. LHASA version 1 categorizes the world’s land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015–2018, with the years 2019–2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.

XGBoos↗
NASA NTRS2020

Mechanisms Associated with Daytime and Nighttime Heat Waves over the Contiguous United States

Heat waves are extreme climate events that have the potential to cause immense stress on human health, agriculture, and energy systems, so understanding the processes leading to their onset is crucial. There is no single accepted definition for heat waves, but they are generally described as a sustained amount of time where temperature exceeds a local threshold. Multiple different temperature variables are potentially relevant, as high values of both daily maximum (Tmax) and minimum (Tmin) temperatures can be detrimental to human health. In this study, we focus explicitly on the different mechanisms associated with summertime heat waves manifested during daytime versus nighttime hours over the contiguous United States. Heat waves are examined using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Over 1980–2018, the increase in the number of heat wave days per summer was generally stronger for nighttime heat wave days than daytime heat wave days, with localized regions of significant positive trends. Processes linked with daytime and nighttime heat waves are identified through composite analysis of precipitation, soil moisture, clouds, humidity and fluxes of heat and moisture. Daytime heat waves are associated with dry conditions, reduced cloud cover, and increased sensible heating. Mechanisms leading to nighttime heat waves differ regionally across the US, but they are typically associated with increased clouds, humidity and/or low-level temperature advection. In the Midwest US, enhanced moisture is transported from the Gulf of Mexico during nighttime heat waves.

Extreme events↗