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NASA NTRS2014

Bias Correction for Assimilation of Retrieved AIRS Profiles of Temperature and Humidity

Atmospheric Infrared Sounder (AIRS) is a hyperspectral radiometer aboard NASA's Aqua satellite designed to measure atmospheric profiles of temperature and humidity. AIRS retrievals are assimilated into the Weather Research and Forecasting (WRF) model over the North Pacific for some cases involving "atmospheric rivers". These events bring a large flux of water vapor to the west coast of North America and often lead to extreme precipitation in the coastal mountain ranges. An advantage of assimilating retrievals rather than radiances is that information in partly cloudy fields of view can be used. Two different Level 2 AIRS retrieval products are compared: the Version 6 AIRS Science Team standard retrievals and a neural net retrieval from MIT. Before assimilation, a bias correction is applied to adjust each layer of retrieved temperature and humidity so the layer mean values agree with a short-term model climatology. WRF runs assimilating each of the products are compared against each other and against a control run with no assimilation. This paper will describe the bias correction technique and results from forecasts evaluated by validation against a Total Precipitable Water (TPW) product from CIRA and against Global Forecast System (GFS) analyses.

Blankenship, Clay↗
NASA NTRS2014

Bias Correction for Assimilation of Retrieved AIRS Profiles of Temperature and Humidity

The Atmospheric Infrared Sounder (AIRS) is a hyperspectral radiometer aboard NASA's Aqua satellite designed to measure atmospheric profiles of temperature and humidity. AIRS retrievals are assimilated into the Weather Research and Forecasting (WRF) model over the North Pacific for some cases involving "atmospheric rivers". These events bring a large flux of water vapor to the west coast of North America and often lead to extreme precipitation in the coastal mountain ranges. An advantage of assimilating retrievals rather than radiances is that information in partly cloudy fields of view can be used. Two different Level 2 AIRS retrieval products are compared: the Version 6 AIRS Science Team standard retrievals and a neural net retrieval from MIT. Before assimilation, a bias correction is applied to adjust each layer of retrieved temperature and humidity so the layer mean values agree with a short‐term model climatology. WRF runs assimilating each of the products are compared against each other and against a control run with no assimilation. Forecasts are against ERA reanalyses.

Blakenship, Clay↗
NASA NTRS2015

The Influence of Sea Ice on Arctic Low Cloud Properties and Radiative Effects

The Arctic is one of the most climatically sensitive regions of the Earth. Climate models robustly project the Arctic to warm 2-3 times faster than the global mean surface temperature, termed polar warming amplification (PWA), but also display the widest range of surface temperature projections in this region. The response of the Arctic to increased CO2 modulates the response in tropical and extra-tropical regions through teleconnections in the atmospheric circulation. An increased frequency of extreme precipitation events in the northern mid-latitudes, for example, has been linked to the change in the background equator-to-pole temperature gradient implied by PWA. Understanding the Arctic climate system is therefore important for predicting global climate change. The ice albedo feedback is the primary mechanism driving PWA, however cloud and dynamical feedbacks significantly contribute. These feedback mechanisms, however, do not operate independently. How do clouds respond to variations in sea ice? This critical question is addressed by combining sea ice, cloud, and radiation observations from satellites, including CERES, CloudSAT, CALIPSO, MODIS, and microwave radiometers, to investigate sea ice-cloud interactions at the interannual timescale in the Arctic. Cloud characteristics are strongly tied to the atmospheric dynamic and thermodynamic state. Therefore, the sensitivity of Arctic cloud characteristics, vertical distribution and optical properties, to sea ice anomalies is computed within atmospheric dynamic and thermodynamic regimes. Results indicate that the cloud response to changes in sea ice concentration differs significantly between atmospheric state regimes. This suggests that (1) the atmospheric dynamic and thermodynamic characteristics and (2) the characteristics of the marginal ice zone are important for determining the seasonal forcing by cloud on sea ice variability.

Taylor, Patrick C.↗
NASA NTRS2015

Vegetation Cover Change in the Upper Kings River Basin of the Sierra Nevada Detected Using Landsat Satellite Image Analysis

The Sierra Nevada of California is a region where large wildfires have been suppressed for over a century. A detailed geographic record of recent changes in vegetation cover across the Sierra Nevada remains a gap that can be filled with satellite remote sensing data. Results from Landsat image analysis over the past 25 years in the Upper Kings River basin showed that consistent, significant increases in the normalized difference vegetation index (NDVI) have not extended above 2000 m elevation, where cold temperatures presumably limit the growing season. Moreover, mean increases in NDVI since 1986 at elevations below 2000 m (which cover about half of the total basin area) have not exceeded 9%, even in the most extreme precipitation yearly comparisons. NDVI has decreased significantly at elevations above 2000 m throughout the basin in relatively wet year comparisons since the mid-1980s. These findings conflict with any assumptions that ET fluxes and river flows downstream could have been markedly altered by vegetation change over most of the Upper Kings River basin in recent decades.

normalized difference vegetation index (NDVI)↗
NASA NTRS2016

Near Real-Time Flood Monitoring and Impact Assessment Systems

Floods are the costliest natural disaster, causing approximately 6.8 million deaths in the twentieth century alone. Worldwide economic flood damage estimates in 2012 exceed $19 Billion USD. Extended duration floods also pose longer term threats to food security, water, sanitation, hygiene, and community livelihoods, particularly in developing countries. Projections by the Intergovernmental Panel on Climate Change (IPCC) suggest that precipitation extremes, rainfall intensity, storm intensity, and variability are increasing due to climate change. Increasing hydrologic uncertainty will likely lead to unprecedented extreme flood events. As such, there is a vital need to enhance and further develop traditional techniques used to rapidly assess flooding and extend analytical methods to estimate impacted population and infrastructure. Measuring flood extent in situ is generally impractical, time consuming, and can be inaccurate. Remotely sensed imagery acquired from space-borne and airborne sensors provides a viable platform for consistent and rapid wall-to-wall monitoring of large flood events through time. Terabytes of freely available satellite imagery are made available online each day by NASA, ESA, and other international space research institutions. Advances in cloud computing and data storage technologies allow researchers to leverage these satellite data and apply analytical methods at scale. Repeat-survey earth observations help provide insight about how natural phenomena change through time, including the progression and recession of floodwaters. In recent years, cloud-penetrating radar remote sensing techniques (e.g., Synthetic Aperture Radar) and high temporal resolution imagery platforms (e.g., MODIS and its 1-day return period), along with high performance computing infrastructure, have enabled significant advances in software systems that provide flood warning, assessments, and hazard reduction potential. By incorporating social and economic data, researchers can develop systems that automatically quantify the socioeconomic impacts resulting from flood disaster events.

Ahamed, Aakash↗
NASA NTRS2016

Near Real-Time Flood Monitoring and Impact Assessment Systems

Floods are the costliest natural disaster (United Nations 2004), causing approximately6.8 million deaths in the twentieth century alone (Doocy et al. 2013).Worldwide economic flood damage estimates in 2012 exceed $19 Billion USD(Munich Re 2013). Extended duration floods also pose longer term threats to food security, water, sanitation, hygiene, and community livelihoods, particularly in developing countries (Davies et al. 2014).Projections by the Intergovernmental Panel on Climate Change (IPCC) suggest that precipitation extremes, rainfall intensity, storm intensity, and variability are increasing due to climate change (IPCC 2007). Increasing hydrologic uncertainty will likely lead to unprecedented extreme flood events. As such, there is a vital need to enhance and further develop traditional techniques used to rapidly assessflooding and extend analytical methods to estimate impacted population and infrastructure.

flood↗
NASA NTRS2019

Water Vapour Adjustments and Responses Differ Between Climate Drivers

Water vapour in the atmosphere is the source of a major climate feedback mechanism and potential increases in the availability of water vapour could have important consequences for mean and extreme precipitation. Future precipitation changes further depend on how the hydrological cycle responds to different drivers of climate change, such as greenhouse gases and aerosols. Currently, neither the total anthropogenic influence on the hydrological cycle nor that from individual drivers is constrained sufficiently to make solid projections. We investigate how integrated water vapour (IWV) responds to different drivers of climate change. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulfate have been perturbed separately. While the global-mean IWV is usually assumed to increase by 7% per kelvin of surface temperature change, we find that the feedback response of IWV differs somewhat between drivers. Fast responses, which include the initial radiative effect and rapid adjustments to an external forcing, amplify these differences. The resulting net changes in IWV range from 6.4±0.9%K(exp -1) for sulfate to 9.8±2%K(exp -1) for BC. We further calculate the relationship between global changes in IWV and precipitation, which can be characterized by quantifying changes in atmospheric water vapour lifetime. Global climate models simulate a substantial increase in the lifetime, from 8.2±0.5 to 9.9±0.7d between 1986-2005 and 2081-2100 under a high-emission scenario, and we discuss to what extent the water vapour lifetime provides additional information compared to analysis of IWV and precipitation separately. We conclude that water vapour lifetime changes are an important indicator of changes in precipitation patterns and that BC is particularly efficient in prolonging the mean time, and therefore likely the distance, between evaporation and precipitation.

Hodnebrog, Øivind↗
NASA NTRS2020

Charles River Watershed Water Resources: Assessing Flooding Vulnerability to Assist High Water Intervention and Urban Planning Programs in the Charles River Watershed

The Charles River watershed intersects 35 municipalities within the Boston Metropolitan Area and has a population of 1.2 million, making it one of the most densely populated watersheds in New England. In recent years, the watershed has observed higher rates of flood inundation, mainly due to increased development, extreme precipitation events, and increased surface runoff. As the frequency of flood events increases and a changing climate poses an ongoing threat to local communities, governments, and organizations in Massachusetts need accurate flood risk assessments. This project partnered with the Charles River Watershed Association, the Town of Natick’s Office of Sustainability, and the Massachusetts Audubon Society to assess the potential for watershed degradation, flood vulnerability, and flood susceptibility in the watershed. The team used Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), and Sentinel-2 MultiSpectral Instrument (MSI) to assess the feasibility of identifying flood events using remote sensing. After identifying images that overlapped with the reported flood events, the team concluded that it was not feasible to use Earth observation data to detect localized flooding. Instead, the Federal Emergency Management Agency (FEMA) 100-year floodplain was used as a proxy for areas where flooding may occur. The team used statistical analysis and supervised classification to develop a flood susceptibility map, incorporating factors like soil drainage, height above nearest drainage, and topographic wetness index. This was overlaid with demographic and socioeconomic data to create a flood vulnerability map. The flood susceptibility map captured over 2/3 of reported flood events in the watershed, an improvement over the 1/3 of events captured by the FEMA 100-year and 500-year floodplain maps.

DEVELOP Project Summary↗
NASA NTRS2021

Strong Regional Influence of Climatic Forcing Datasets on Global Crop Model Ensembles

We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.

Agricultural Model Intercomparison and Improvement↗
NASA NTRS2021

Satellites Support Disaster Response to Storm-Driven Landslides

High winds and flooding storm surges driven by tropical cyclones cause some of the deadliest and most damaging weather-related conditions around the world. The rainfall that cyclones bring compounds these conditions and, in hilly or mountainous areas, can trigger landslides that cause even more widespread and devastating impacts. When extreme precipitation occurs over short time frames, hillslopes may become saturated and critically unstable. The most intense storms can trigger thousands of landslides in mountainous areas, as was dramatically illustrated in Puerto Rico in September 2017, when Hurricane Maria’s rains left the landscape scarred by roughly 40,000 landslides. Before and during a major cyclone, disaster responders need information about where landslides are likely to occur. In the aftermath, locating landslides quickly helps authorities direct resources to where they are most needed to save people and critical infrastructure. However, this information is often unavailable during an event response or is presented only for small regions, constraining the effectiveness of response efforts.

Robert Emberson↗
NASA NTRSDate not supplied

Assessing Flooding Vulnerability to Assist High Water Intervention and Urban Planning Programs in the Charles River Watershed with NASA DEVELOP

The Charles River watershed intersects 35 municipalities within the Boston Metropolitan Area and has a total population of 1.2 million, making it one of the most densely populated watersheds in New England. In recent years, the watershed has observed higher rates of flood inundation, mainly due to increased development, extreme precipitation events, and increased surface runoff. As the frequency of flooding events increases and a changing climate poses an ongoing threat to local communities, governments and organizations in Massachusetts are in need of accurate flood risk assessments. NASA DEVELOP partnered with the Charles River Watershed Association, the Town of Natick’s Office of Sustainability, and the Massachusetts Audubon Society to assess flood vulnerability and susceptibility in the watershed. The team used Landsat 5 Thematic Mapper, Landsat 8 Operational Land Imager, Sentinel-1 C-Band Synthetic Aperture Radar, and Sentinel-2 MultiSpectral Instrument to assess the feasibility of identifying the extent of past flood events using remote sensing. After identifying images that overlapped with reported flood events, the team concluded that it was not feasible to use Earth observation data to detect localized flooding in the time available for this study. Instead, the Federal Emergency Management Agency (FEMA) 100-year floodplain was used as a proxy for areas where flooding may occur. The team used statistical regression analysis and validation and supervised classification to develop a flood susceptibility map, incorporating several flood conditioning factors. The susceptibility maps were calibrated to various thresholds, including two that highlight hypothetical flooding under more liberal and more conservative planning scenarios. These were overlaid with demographic and socioeconomic data to create flood vulnerability maps. The team’s flood susceptibility maps showed an improvement in capturing known flood events over the FEMA 100-year and 500-year floodplain maps. These results will be improved with the addition of stormwater drainage mapping and precipitation data. Results can be used to fill in the gaps to help the stakeholders understand their communities’ vulnerability and susceptibility to flooding and improve their preparedness plans.

Trista Brophy↗
NASA NTRS2021

Capturing the Footprints of Ground Motion in the Spatial Distribution of Rainfall-Induced Landslides

The coupled effect of earthquakes and rainfall is rarely investigated in landslide susceptibility assessments although it could be crucial to predict landslide occurrences. This is even more critical in the context of early warning systems and especially in cases of extreme precipitation regimes in post-seismic conditions, where the rock masses are already damaged due to the ground shaking. Here, we investigate this concept by accounting for the legacy of seismic ground shaking in rainfall-induced landslide (RFIL) scenarios. We do this to identify whether ground shaking plays a role in the susceptibility to post-seismic rainfall-induced landslides and to identify whether this legacy effect persists through time. With this motivation, we use binary logistic regression and examine time series of landslides associated with four earthquakes occurred in Indonesia: 2012 Sulawesi (M(sub w) = 6.3), 2016 Reuleut (M(sub w) = 6.5), 2017 Kasiguncu (M(sub w) = 6.6) and 2018 Palu (M(sub w) = 7.5) earthquakes. The dataset includes one co-seismic and three post-seismic landslide inventories for each earthquake. We use the peak ground acceleration map of the last strongest earthquake in each case as a predisposing factor of landslides representing the effect of ground shaking. We observe that, at least for the study areas under consideration and in a probabilistic context, the earthquake legacy contributes to increase the post-seismic RFIL susceptibility. This positive contribution decays through time. Specifically, we observe that ground motion is a significant predisposing factor controlling the spatial distribution of RFIL in the post-seismic period 110 days after an earthquake. We also show that this effect dissipates within 3 years at most.

Landslides↗
NASA NTRS2014

Comparing Evaporative Sources of Terrestrial Precipitation and Their Extremes in MERRA Using Relative Entropy

A quasi-isentropic back trajectory scheme is applied to output from the Modern Era Retrospective-analysis for Research and Applications and a land-only replay with corrected precipitation to estimate surface evaporative sources of moisture supplying precipitation over every ice-free land location for the period 1979-2005. The evaporative source patterns for any location and time period are effectively two dimensional probability distributions. As such, the evaporative sources for extreme situations like droughts or wet intervals can be compared to the corresponding climatological distributions using the method of relative entropy. Significant differences are found to be common and widespread for droughts, but not wet periods, when monthly data are examined. At pentad temporal resolution, which is more able to isolate floods and situations of atmospheric rivers, values of relative entropy over North America are typically 50-400 larger than at monthly time scales. Significant differences suggest that moisture transport may be the key to precipitation extremes. Where evaporative sources do not change significantly, it implies other local causes may underlie the extreme events.

Climatological Distributions↗
NASA NTRS2013

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Hurricanes↗
NASA NTRS2019

Extreme Wet and Dry Conditions Affected Differently by Greenhouse Gases and Aerosols

Global warming due to greenhouse gases and atmospheric aerosols alter precipitation rates, but the influence on extreme precipitation by aerosols relative to greenhouse gases is still not well known. Here we use the simulations from the Precipitation Driver and Response Model Intercomparison Project that enable us to compare changes in mean and extreme precipitation due to greenhouse gases with those due to black carbon and sulfate aerosols, using indicators for dry extremes as well as for moderate and very extreme precipitation. Generally, we find that the more extreme a precipitation event is, the more pronounced is its response relative to global mean surface temperature change, both for aerosol and greenhouse gas changes. Black carbon (BC) stands out with distinct behavior and large differences between individual models. Dry days become more frequent with BC-induced warming compared to greenhouse gases, but so does the intensity and frequency of extreme precipitation. An increase in sulfate aerosols cools the surface and thereby the atmosphere, and thus induces a reduction in precipitation with a stronger effect on extreme than on mean precipitation. A better understanding and representation of these processes in models will provide knowledge for developing strategies for both climate change and air pollution mitigation.

Sillmann, Jana↗
NASA NTRS2019

Negative extreme events in gross primary productivity and their drivers in China during the past three decades

Climate extremes have remarkable impacts on ecosystems and are expected to increase with future global warming. However, only few studies have focused on the ecological extreme events and their drivers in China. In this study, we carried out an analysis of negative extreme events in gross primary productivity (GPP) in China and the sub-regions during 1982–2015, using monthly GPP simulated by 12 process-based models (TRENDYv6) and an observation-based model (Yao-GPP). Extremes were defined as the negative 5th percentile of GPP anomalies, which were further merged into individual extreme events using a three-dimensional contiguous algorithm. Spatio-temporal patterns of negative GPP anomalies were analyzed by taking the 1000 largest extreme events into consideration. Results showed that the effects of extreme events decreased annual GPP by 2.8% (i.e. 208 TgC/year) in TRENDY models and 2.3% (i.e. 151 TgC/year) in Yao-GPP. Hotspots of extreme GPP deficits were mainly observed in North China (−53 gC/sq.m year) in TRENDY models and Northeast China (−42 gC/sq.m year) in Yao-GPP. For China as a whole, attribution analyses suggested that extreme low precipitation was associated with 40%–50% of extreme negative GPP events. Most events in northern and western China could be explained by meteorological droughts (i.e. low precipitation) while GPP extreme events in southern China were more associated with temperature extremes, in particular with cold spells. GPP was revealed to be much more sensitive to heat/drought than to cold/wet extreme events. Combined with projected changes in climate extremes in China, GPP negative anomalies caused by drought events in northern China and by temperature extremes in southern China might be more prominent in the future.

Climate change↗
NASA NTRS2020

Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall

The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional ‘‘grid-by-grid analysis,’’ the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for ‘‘hybrid’’ data-driven and physics-driven estimates in order to make optimal usage of satellite observations.

extreme events↗
NASA NTRS2021

A Dusty Atmospheric River Brings Floods to the Middle East

Torrential rainfall and rapid snowmelt in April 2017 caused deadly floods in northwestern Iran. An atmospheric river (AR), propagating across the Middle East and North Africa, was found responsible for this extreme event. The snowmelt was triggered by precipitation and warm advection associated with the AR. Total satellite-based rainfall for April 2017 was moderately below normal, suggesting that a heavy flood can happen during dry years. The AR was fed by moisture from the Mediterranean and Red Seas. Despite its adverse societal consequences, this event was beneficial to the recovery of the desiccating Lake Urmia. The impacts of this AR were not limited to flooding; it also facilitated dust transport to the region. This distinct characteristic of the ARs in the Middle East is attributed to major mineral dust sources located along their pathways. This event was reasonably predicted at 7-day lead time, crucially important for successful early warning systems.

Atmospheric river↗