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NASA NTRS2018

The Arctic Sea Ice Cover of 2016: A Year of Record-Low Highs and High-Than-Expected Lows

The Arctic sea ice cover of 2016 was highly noteworthy, as it featured record low monthly sea ice extents at the start of the year but a summer (September) extent that was higher than expected by most seasonal forecasts. Here we explore the 2016 Arctic sea ice state in terms of its monthly sea ice cover, placing this in the context of the sea ice conditions observed since 2000. We demonstrate the sensitivity of monthly Arctic sea ice extent and area estimates, in terms of their magnitude and annual rankings, to the ice concentration input data (using two widely used datasets) and to the averaging methodology used to convert concentration to extent (daily or monthly extent calculations). We use estimates of sea ice area over sea ice extent to analyse the relative “compactness†of the Arctic sea ice cover, highlighting anomalously low compactness in the summer of 2016 which contributed to the higher-than-expected September ice extent. Two cyclones that entered the Arctic Ocean during August appear to have driven this low-concentration/compactness ice cover but were not sufficient to cause more widespread melt-out and a new record-low September ice extent. We use concentration budgets to explore the regions and processes (thermodynamics/ dynamics) contributing to the monthly 2016 extent/area estimates highlighting, amongst other things, rapid ice intensification across the central eastern Arctic through September. Two different products show significant early melt onset across the Arctic Ocean in 2016, including record-early melt onset in the North Atlantic sector of the Arctic. Our results also show record-late 2016 freeze-up in the central Arctic, North Atlantic and the Alaskan Arctic sector in particular, associated with strong sea surface temperature anomalies that appeared shortly after the 2016 minimum (October onwards). We explore the implications of this low summer ice compactness for seasonal forecasting, suggesting that sea ice area could be a more reliable metric to forecast in this more seasonal, "New Arctic", sea ice regime.

sea ice↗
NASA NTRS2000

Sensitivity of Spacebased Microwave Radiometer Observations to Ocean Surface Evaporation

Ocean surface evaporation and the latent heat it carries are the major components of the hydrologic and thermal forcing on the global oceans. However, there is practically no direct in situ measurements. Evaporation estimated from bulk parameterization methods depends on the quality and distribution of volunteer-ship reports which are far less than satisfactory. The only way to monitor evaporation with sufficient temporal and spatial resolutions to study global environment changes is by spaceborne sensors. The estimation of seasonal-to-interannual variation of ocean evaporation, using spacebased measurements of wind speed, sea surface temperature (SST), and integrated water vapor, through bulk parameterization method,s was achieved with reasonable success over most of the global ocean, in the past decade. Because all the three geophysical parameters can be retrieved from the radiance at the frequencies measured by the Scanning Multichannel Microwave Radiometer (SMMR) on Nimbus-7, the feasibility of retrieving evaporation directly from the measured radiance was suggested and demonstrated using coincident brightness temperatures observed by SMMR and latent heat flux computed from ship data, in the monthly time scale. However, the operational microwave radiometers that followed SMMR, the Special Sensor Microwave/Imager (SSM/I), lack the low frequency channels which are sensitive to SST. This low frequency channels are again included in the microwave imager (TMI) of the recently launched Tropical Rain Measuring Mission (TRMM). The radiance at the frequencies observed by both TMI and SSM/I were simulated through an atmospheric radiative transfer model using ocean surface parameters and atmospheric temperature and humidity profiles produced by the reanalysis of the European Center for Medium Range Weather Forecast (ECMWF). From the same ECMWF data set, coincident evaporation is computed using a surface layer turbulent transfer model. The sensitivity of the radiance to evaporation over various seasons and geographic locations are examined. The microwave frequencies with radiance that are significant correlated with evaporation are identify and capability of estimating evaporation directly from TMI will be discussed.

Liu, Timothy W.↗
NASA NTRS2012

Observing Changing Ecological Diversity in the Anthropocene

As the world enters the Anthropocene, the planet's environment is changing rapidly, putting critical ecosystem services at risk. Understanding and forecasting how ecosystems will change over the coming decades requires understanding the sensitivity of species to environmental change. The extant distribution of species and functional groups contains valuable information about the performance of different species in different environments. However, with high rates of environmental change, information inherent in ranges of many species will disappear, since that information exists only under quasi-equilibrium conditions. The information content of distributional data obtained now is greater than data obtained in the future. New remote sensing technologies can map chemical and structural traits of plant canopies and allow inference of trait and in many cases, species ranges. Current satellite remote sensing data can only produce relatively simple classifications, but new techniques have dramatically higher biological information content.

environmental changes↗
NASA NTRS2002

Land Surface Data Assimilation and the Northern Gulf Coast Land/Sea Breeze

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The sea/land breeze is a well-documented mesoscale circulation that affects many coastal areas of the world including the northern Gulf Coast of the United States. The focus of this paper is to examine how the satellite assimilation technique impacts the simulation of a sea breeze circulation observed along the Mississippi/Alabama coast in the spring of 2001. The technique is implemented within the PSU/NCAR MM5 V3-4 and applied on a 4-km domain for this particular application. It is recognized that a 4-km grid spacing is too coarse to explicitly resolve the detailed, mesoscale structure of sea breezes. Nevertheless, the model can forecast certain characteristics of the observed sea breeze including a thermally direct circulation that results from differential low-level heating across the land-sea interface. Our intent is to determine the sensitivity of the circulation to the differential land surface forcing produced via the assimilation of GOES skin temperature tendencies. Results will be quantified through statistical verification techniques.

Lapenta, William M.↗
NASA NTRS2003

Application of Land Surface Data Assimilation to Simulations of Sea Breeze Circulations

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite- observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The sea/land breeze is a well-documented mesoscale circulation that affects many coastal areas of the world including the northern Gulf Coast of the United States. The focus of this paper is to examine how the satellite assimilation technique impacts the simulation of a sea breeze circulation observed along the Mississippi/Alabama coast in the spring of 2001. The technique is implemented within the PSUNCAR MM5 V3-5 and applied at spatial resolutions of 12- and 4-km. It is recognized that even 4-km grid spacing is too coarse to explicitly resolve the detailed, mesoscale structure of sea breezes. Nevertheless, the model can forecast certain characteristics of the observed sea breeze including a thermally direct circulation that results from differential low-level heating across the land-sea interface. Our intent is to determine the sensitivity of the circulation to the differential land surface forcing produced via the assimilation of GOES skin temperature tendencies. Results will be quantified through statistical verification techniques.

Mackaro, Scott↗
NASA NTRS2012

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the surface of 0.25 g / cubic m and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Sensitivity analyses were performed to better ascertain the relationships between multifrequency microwave and millimeter-wave sensor observations and the falling snow/underlying field of view. In addition, thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types were studied. Results will be presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz.

Gail Skofronick-Jackson↗
NASA NTRS2001

The Future of Satellite-based Lightning Detection

The future of satellite-based optical lightning detection, beyond the end of the current TRMM mission, is discussed. Opportunities for new low-earth orbit missions are reviewed. The potential for geostationary observations is significant; such observations provide order-of-magnitude gains in sampling and data efficiency over existing satellite convective observations. The feasibility and performance (resolution, sensitivity) of geostationary measurements using current technology is discussed. In addition to direct and continuous hemispheric observation of lighting, geostationary measurements have the potential (through data assimilation) to dramatically improve short and medium range forecasts, offering benefits to prediction of NOx productions and/or vertical transport.

Bocippio, Dennis J.↗
NASA NTRS2018

Impact of Assimilating AIRS Cloud-Cleared Radiances on Atmospheric Dynamics and Polar Low Representation at High Latitudes

This study explores the sensitivity of planetary boundary layer height and related atmospheric dynamics to the assimilation of cloud-cleared AIRS (Atmospheric Infrared Sounder) radiances in the Goddard Earth Observing System (GEOS, version 5) data assimilation and forecast system during the boreal fall 2014 season using observing system experiments (OSEs). Examined here are comparisons between the current, operational approach of assimilating AIRS clear-sky radiances against the assimilation of cloud-cleared radiances (CCR). In polar regions, assimilation of AIRS CCRs is particularly beneficial because of the sparsity of conventional observations and the prevalence of extended low-level stratus cloud cover, which limit the ingestion of clear-sky data. Assimilation of hyperspectral infrared information from AIRS over the Arctic region slightly modifies the lower midtropospheric temperature structure, which in turn contributes to adjustments in geopotential height, affecting the baroclinic instability properties over the entire hemisphere and explaining the overall improvement in global forecast skill. In addition, it is shown that the assimilation of CCRs benefits the representation of convectively-driven small-scale cyclones at high latitudes in the same way as previously noted for tropical cyclones. Specifically, assimilation of CCRs create a temperature dipole over the top of meteorologically active and strongly convective systems such as polar, arctic, and antarctic lows, which helps constrain the analyzed representation of their scale and vertical structure.

Mcgrath-Spangler, E. L.↗
NASA NTRS1982

Weather radar research at the USA's storm laboratory

Radar research that is directed toward improving storm forecasts and hazard warnings and studying lightning is discussed. The two moderately sensitive Doppler weather radars in central Oklahoma, with their wide dynamic range, have demonstrated the feasibility of mapping wind fields in all weather conditions from the clear skies of quiescent air and disturbed prestorm air near the earth's surface to the optically opaque interior of severe and sometimes tornadic thunderstorms. Observations and analyses of Doppler weather radar data demonstrate that improved warning of severe storm phenomena and improved short-term forecast of storms may be available when Doppler techniques are well integrated into the national network of weather radars. When used in combination with other sensors, it provides an opportunity to learn more about the complex interrelations between the wind, water, and electricity in storms.

Doviak, R. J.↗
NASA NTRS1989

Sensitivity of mesoscale-model forecast skill to some initial-data characteristics, data density, data position, analysis procedure and measurement error

The effects of horizontal and vertical data resolution, data density, data location, different objective analysis algorithms, and measurement error on mesoscale-forecast accuracy are studied with observing-system simulation experiments. Domain-averaged errors are shown to generally decrease with time. It is found that the vertical distribution of error growth depends on the initial vertical distribution of the error itself. Larger gravity-inertia wave noise is produced in forecasts with coarser vertical data resolution. The use of a low vertical resolution observing system with three data levels leads to more forecast errors than moderate and high vertical resolution observing systems with 8 and 14 data levels. Also, with poor vertical resolution in soundings, the initial and forecast errors are not affected by the horizontal data resolution.

Warner, Thomas T.↗
NASA NTRS1982

An assessment of the FGGE satellite observing system during SOP-1

The sensitivity of a Goddard Laboratory for Atmospheric Sciences global objective analysis cycle to the addition of FGGE level II-b data is assessed. The GOAS system comprises a predictive continuity provided by a model first-guess forecast integrated from a previous forecast and updated by data gathered in the interim. FGGE data originated in the Jan.-Mar. 1979 period and were acquired by rawinsondes, pilot balloons, surface stations, satellites, ships, and drifting buoys deployed during SOP-1. Focussing on 2-5 and 8-day forecasts, comparisons were made of the 6 hr forecast error at the 300 mb height in three experiments using all, no-satellite (NOSAT), and without rawinsondes or pilot balloons modes. Larger errors occurred in the case of NOSAT, while significant corrections to the GOAS predictions were noted using all the FGGE data. It was concluded that all forecasts were improved by inclusion of full FGGE data sets, including forecasting beyond one week.

Halem, M.↗
NASA NTRS2018

NASA's Experiences with Microwave Radiometers from Ground to Space

Passive microwave sensing is sensitive to a wide variety of geophysical parameters in the atmosphere, oceans, cryosphere, and on land. For example, spaceborne passive microwave atmospheric sounders provide the highest-impact observations for state-of-the-art weather forecast models used by meteorological agencies around the world. The ability of passive microwave sensors (radiometers) to observe day or night through cloud cover and other obstacles (wavelength dependent, of course) partially compensates for their sometimes large footprint size compared to other sensor types. Examples will be presented of radiometers deployed on the ground, on aircraft, and on satellites. Each of these environments presents different challenges in obtaining accurate observations. And while many standard radiometer designs and techniques have been developed, every user re-discovers that even the "standard" approach requires careful attention in order to achieve full potential.

calibration↗
NASA NTRS1981

Weather and climate

Highlights of recent progress and the directions of future advances in the application of space observations to weather and climate are reviewed. In mesometeorology and severe storms, satellite stereography of cloud topography and temperature profiling from GOES-VAS promise dramatic developments in both nowcasting and prediction. In global weather, the initial results from the year long Global Weather Experiment conclusively demonstrate the enhanced forecast skill emanating from the use of satellite data, especially cloud track winds and temperature profiles. In climate, empirical studies and numerical experiments point to the feasibility of useful climate predictions on monthly and seasonal time scales. They also indicate the kinds of surface boundary conditions to which climate is sensitive and which need to be observed from space.

Atlas, D.↗
NASA NTRS1993

Observational and modeling studies of the influence of sea ice anomalies on atmospheric circulation

The effects of sea ice on short-term climate variability are studied by comparing the speculated and model-derived climatic roles of sea ice with the observational evidence. It is concluded that sea ice variations have large local impacts on the lower atmosphere which can be useful in local forecasting at the 30-90 day range. Observational studies are found to be severely constrained by the fact that atmospheric forcing plays a major role in the development of sea ice anomalies. Global climate models exhibit significant sensitivities to large changes of sea ice coverage. While the responses of different models are similar in many respects, there are inconsistencies among the models in the changes of some fundamental variables.

Walsh, John E.↗
NASA NTRS2013

SMAP Radar Processing and Calibration

The Soil Moisture Active Passive (SMAP) mission is part of the NASA space-based Earth observation program, and consists of an L-band radar and radiometer scheduled for launch into sun synchronous orbit in late 2014. A joint effort of the Jet Propulsion Laboratory (JPL) and the Goddard Space Flight Center (GSFC), the SMAP mission draws heavily on the design and risk reduction heritage of the Hydrosphere State (Hydros) mission [1], [2]. The SMAP science and applications objectives are to: 1) understand processes that link the terrestrial water, energy and carbon cycles, 2) estimate global water and energy fluxes at the land surface, 3) quantify net carbon flux in boreal landscapes, 4) enhance weather and climate forecast skill, and 5) develop improved flood prediction and drought monitoring capability. To meet these science objectives, SMAP ground processing will combine the attributes of the radar and radiometer observations (in terms of their spatial resolution and sensitivity to soil moisture, surface roughness, and vegetation) to estimate soil moisture with 4% volumetric accuracy at a resolution of 10 km, and freeze-thaw state at a resolution of 1-3 km. Model sensitivities translate the soil moisture accuracy to a radar backscatter accuracy of 1 dB (1 sigma) at 3 km resolution and a brightness temperature accuracy of 1.3 K at 40 km resolution. This paper will describe the level 1 radar processing and calibration challenges and the choices made so far for the algorithms and software implementation.

Synthetic Aperture Radar (SAR),↗
NASA NTRS2009

Winter QPF Sensitivities to Snow Parameterizations and Comparisons to NASA CloudSat Observations

Steady increases in computing power have allowed for numerical weather prediction models to be initialized and run at high spatial resolution, permitting a transition from larger scale parameterizations of the effects of clouds and precipitation to the simulation of specific microphysical processes and hydrometeor size distributions. Although still relatively coarse in comparison to true cloud resolving models, these high resolution forecasts (on the order of 4 km or less) have demonstrated value in the prediction of severe storm mode and evolution and are being explored for use in winter weather events . Several single-moment bulk water microphysics schemes are available within the latest release of the Weather Research and Forecast (WRF) model suite, including the NASA Goddard Cumulus Ensemble, which incorporate some assumptions in the size distribution of a small number of hydrometeor classes in order to predict their evolution, advection and precipitation within the forecast domain. Although many of these schemes produce similar forecasts of events on the synoptic scale, there are often significant details regarding precipitation and cloud cover, as well as the distribution of water mass among the constituent hydrometeor classes. Unfortunately, validating data for cloud resolving model simulations are sparse. Field campaigns require in-cloud measurements of hydrometeors from aircraft in coordination with extensive and coincident ground based measurements. Radar remote sensing is utilized to detect the spatial coverage and structure of precipitation. Here, two radar systems characterize the structure of winter precipitation for comparison to equivalent features within a forecast model: a 3 GHz, Weather Surveillance Radar-1988 Doppler (WSR-88D) based in Omaha, Nebraska, and the 94 GHz NASA CloudSat Cloud Profiling Radar, a spaceborne instrument and member of the afternoon or "A-Train" of polar orbiting satellites tasked with cataloguing global cloud characteristics. Each system provides a unique perspective. The WSR-88D operates in a surveillance mode, sampling cloud volumes of Rayleigh scatterers where reflectivity is proportional to the sixth moment of the size distribution of equivalent spheres. The CloudSat radar provides enhanced sensitivity to smaller cloud ice crystals aloft, as well as consistent vertical profiles along each orbit. However, CloudSat reflectivity signatures are complicated somewhat by resonant Mie scattering effects and significant attenuation in the presence of cloud or rain water. Here, both radar systems are applied to a case of light to moderate snowfall within the warm frontal zone of a cold season, synoptic scale storm. Radars allow for an evaluation of the accuracy of a single-moment scheme in replicating precipitation structures, based on the bulk statistical properties of precipitation as suggested by reflectivity signatures.

Molthan, Andrew↗
NASA NTRS2021

A Machine Learning Approach to Objective Identification of Dust in Satellite Imagery

Airborne dust has broad adverse effects on human activity, including aviation, human health, and agriculture. Remote sensing observations are used to detect dust and aerosols in the atmosphere using long established techniques. False color Red-Green-Blue (RGB) imagery using band differences sensitive to dust absorption (Dust RGB) is currently used operationally to assist forecasters and decision-makers in identifying dust at night, but there are still limitations, subjectivity, and nuances to image interpretation making night-time dust identification difficult even for experts. This study applies machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery, band differences sensitive to dust absorption, and Dust RGB color components as inputs to the model. The RF model achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels and 99.96% of no-dust pixels for all dust images in the validation data set. The addition of a single null case to the training data set drastically reduces error in labeling no-dust pixels as dust from 45% to 14.5%. Application of the machine learning model to the April 13–14, 2019 dust event demonstrates the ability of the model to identify dust during night-time hours when visual dust detection is limited by the cooling ground surface characteristics.

dust↗
NASA NTRS2009

Sensitivity of Short-Term Weather Forecasts to Assimilated AIRS Data: Implications for NPOESS Applications

The Atmospheric Infrared Sounder (AIRS) is acting as a heritage and risk reduction instrument for the Cross-track lnfrared Sounder (CrIS) to be flown aboard the NPP and NPOESS satellites. The hyperspectral nature of AIRS and CrIS provides high-quality soundings that, along with their asynoptic observation time over North America, make them attractive sources to fill the spatial and temporal data voids in upper air temperature and moisture measurements for use in data assimilation and numerical weather prediction. Observations from AlRS can be assimilated either as direct radiances or retrieved thermodynamic profiles, and the Short-Term Prediction Research and Transition (SPORT) Center at NASA's Marshall Space Flight Center has used both data types to improve short-term (0-48h), regional forecasts. The purpose of this paper is to share SPORT'S experiences using AlRS radiances and retrieved profiles in regional data assimilation activities by showing that proper handling of issues-including cloud contamination and land emissivity characterization-are necessary to produce optimal analyses and forecasts.

Zavodsky, Bradley↗