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NASA NTRS1999

Observed Impacts

Agricultural applications of El Nino forecasts are already underway in some countries and need to be evaluated or re-evaluated. For example, in Peru, El Nino forecasts have been incorporated into national planning for the agricultural sector, and areas planted with rice and cotton (cotton being the more drought-tolerant crop) are adjusted accordingly. How well are this and other such programs working? Such evaluations will contribute to the governmental and intergovernmental institutions, including the Inter-American Institute for Global Change Research and the US National Ocean and Atmospheric Agency that are fostering programs to aid the effective use of forecasts. This research involves expanding, deepening, and applying the understanding of physical climate to the fields of agronomy and social science; and the reciprocal understanding of crop growth and farm economics to climatology. Delivery of a regional climate forecast with no information about how the climate forecast was derived limits its effectiveness. Explanation of a region's major climate driving forces helps to place a seasonal forecast in context. Then, a useful approach is to show historical responses to previous El Nino events, and projections, with uncertainty intervals, of crop response from dynamic process crop growth models. Regional forecasts should be updated with real-time weather conditions. Since every El Nino event is different, it is important to track, report and advise on each new event as it unfolds.

Rosenzweig, Cynthia↗

DOE OSTI2020

Collar Properties and Measurement Time Confer Minimal Bias Overall on Annual Soil Respiration Estimates in a Global Database

Abstract Measuring the soil‐to‐atmosphere carbon dioxide (CO 2 ) flux (soil respiration, R S ) is important to understanding terrestrial carbon balance and to forecasting climate change. Such measurements are frequently made using measurement collars permanently inserted into the soil surface. However, differences in measurement duration and frequency, as well as collar properties, may lead to biases in the estimation of annual R S . Using a newly updated global R S database (SRDB‐V5), we investigated the annual R S bias associated with five methodological factors: collar height, collar coverage area, collar insertion depth, measurement duration, and measurement frequency. We found that annual R S was negatively correlated with collar insertion depth, consistent with the idea that collar insertion cuts roots and thus reduces R S . Annual R S was also negatively related with collar height and collar coverage area, perhaps because uniform head‐space mixing is difficult to achieve in larger volume chambers; however, these effects were quantitatively small (bias of ~2% to 10% of mean R S ). We found no correlation of measurement duration or measurement frequency with annual R S . These findings suggest that variation in R S methodology generally introduces minimal bias overall. Therefore, compilations of minimally adjusted annual R S measurements provide a reliable resource for synthesis studies, global annual R S modeling, and investigation of how soil carbon responds to climate change.

Jian, Jinshi↗

DOE OSTI2024

Tropical Cyclone Wind Shear-Relative Asymmetry in Reanalyses

Abstract While tropical cyclones (TCs) are axisymmetric vortices to the first order, they often exhibit noteworthy structural asymmetries. These often result from environmental vertical wind shear, which tilts the vortex and induces a wavenumber 1 pattern in the circulation and precipitation fields. Reanalyses and climate models have improved in representing the TC structure and climatology, but their relatively coarse resolution and dependence on parameterized physics cast doubt on their ability to capture the asymmetric TC structure. We perform the most comprehensive process-oriented assessment of TC asymmetry to date in reanalyses. Specifically, we analyze the composite shear-relative TC structure in ERA5 and Climate Forecast System Reanalysis (CFSR), which vary in their resolutions, physical parameterization suites, and data assimilation techniques. These structures are compared with aircraft reconnaissance radar observations. In agreement with the observations, the strongest tangential winds are usually found left-of-shear, while inner core rainfall, ascent, vortex tilt, and low-level inflow are favored directly downshear or in the downshear-left quadrant. Outer rainband convection generally peaks in the downshear-right quadrant. Thermodynamic asymmetries are also apparent, with anomalous low-level moisture right-of-shear, midlevel warmth in the upshear-right quadrant (uptilt), and cloud properties suggestive of a realistic precipitation life cycle from growth to fallout. We also decompose rainfall contributions from the convective parameterization and large-scale cloud schemes and highlight the roles of vorticity advection, buoyancy advection, and diabatic processes in driving asymmetric vertical motions in the inner core and outer rainband regions. Our results suggest that process-level studies of TC asymmetry and TC–wind shear interaction under future warming are viable using climate models. Significance Statement Asymmetries are common in tropical cyclones (TCs), influencing their intensity, track, and hazards. Vertical wind shear often plays a leading-order role in causing these asymmetries. It is uncertain how well asymmetric structures and processes are captured in reanalyses and global climate models (GCMs) with grid spacings of 0.25° and coarser. In this study, we first evaluate TC asymmetry in reanalyses, which have the benefit of being forced by observations. This helps to assess whether the resolutions associated with GCMs sufficiently capture asymmetric structures and processes and motivates upcoming work with free-running GCMs to study how TC asymmetry may change in a warming climate.

Carstens, Jacob D.↗

DOE OSTI2020

Atmosphere-Biosphere Interaction Study (Field Campaign Report)

The objective of this campaign was to collect hyperspectral reflectance measurements and complementary optical and infrared data of land surface (i.e., vegetation and soil) throughout the growing season (i.e., from March to September) at high temporal frequency (e.g., < 30 minutes). To collect the measurements, the EcoSpec system, a tower-based sensor system housing seven optical and infrared sensors that was developed at Argonne National Laboratory, was deployed to the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) atmospheric observatory (within the crop, south of the eddy correlation flux measurement system [ECOR] tower). The EcoSpec system consists of a spectroradiometer having 2,151 channels (350–2,500 nm), red-greenblue (RGB) camera, diffuse radiometer, thermal infrared (TIR) sensors, albedometers, Photochemical Reflectance Index (PRI) sensors, Normalized Difference Vegetation Index (NDVI) sensors, and wetness sensor. The system is designed to collect an array of land surface measurements, including hyperspectral reflectance of plants and soils, RGB photos capturing contextual information of land surface, radiant temperature of sky and land surface, incoming and reflected shortwave radiation, and incoming radiation components (direct and diffuse) as frequent as every minute from up to 12 discrete positions around the tower. This would allow us to capture inherent heterogeneity of the terrestrial ecosystem and to obtain representative spectral signatures of the plant and soil surfaces via averaging those measurements. The SGP site provides advanced atmospheric and other physical measurements that are essential for our investigation of ecosystem functions using hyperspectral reflectance measurements of land surfaces. Located in the mid-latitudes, the SGP site exhibits dynamic seasonality and a wide range of meteorological variability and flux properties that make it ideal for studying interactions between near-surface atmosphere and biosphere across a range of environmental conditions. The atmosphere, plants, and soil control terrestrial carbon and water cycles. To make more accurate climate forecasts, researchers need to understand ecosystem dynamics at the biosphere–atmosphere interface. Even though our ability to forecast ecosystem dynamics and climate at both the regional and global scales has advanced significantly, we still do not know how local phenomena affecting water and carbon fluxes—such as diurnal variations—interrelate with large-scale atmospheric/climate phenomena, and vice versa. The lack of a more thorough understanding of ecosystem dynamics and climate interactions is a key knowledge gap for the improvement of earth system models (ESMs).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Ecological forecasts for marine resource management during climate extremes

Forecasting weather has become commonplace, but as society faces novel and uncertain environmental conditions there is a critical need to forecast ecology. Forewarning of ecosystem conditions during climate extremes can support proactive decision-making, yet applications of ecological forecasts are still limited. We showcase the capacity for existing marine management tools to transition to a forecasting configuration and provide skilful ecological forecasts up to 12 months in advance. The management tools use ocean temperature anomalies to help mitigate whale entanglements and sea turtle bycatch, and we show that forecasts can forewarn of human-wildlife interactions caused by unprecedented climate extremes. We further show that regionally downscaled forecasts are not a necessity for ecological forecasting and can be less skilful than global forecasts if they have fewer ensemble members. Our results highlight capacity for ecological forecasts to be explored for regions without the infrastructure or capacity to regionally downscale, ultimately helping to improve marine resource management and climate adaptation globally.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2013

Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental US (CONUS)

In the middle to high latitude and alpine regions, the seasonal snow pack can dominate the surface energy and water budgets due to its high albedo, low thermal conductivity, high emissivity, considerable spatial and temporal variability, and ability to store and then later release a winters cumulative snowfall (Cohen, 1994; Hall, 1998). With this in mind, the snow drought across the U.S. has raised questions about impacts on water supply, ski resorts and agriculture. Knowledge of various snow pack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project (Mitchell et al., 2004). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions for a future NCEP drought forecast system. Additionally, efforts are currently underway to assimilate remotely-sensed estimates of land-surface states such as snowpack information into NLDAS. It is believed that this assimilation will not only produce improved snowpack states that better represent snow evolving conditions, but will directly improve the monitoring of drought.

MODIS↗

DOE OSTI2024

Tropical Interbasin Interaction as Effective Predictors of Late-Spring Precipitation Variability in the Southern Great Plains

Abstract The southern Great Plains experience fluctuating precipitation extremes that significantly impact agriculture and water management. Despite ongoing efforts to enhance forecast accuracy, the underlying causes of these climatic phenomena remain inadequately understood. This study elucidates the relative influence of the tropical Pacific and Atlantic basins on April–May–June precipitation variability in this region. Our partial ocean assimilation experiments using the Community Earth System Model unveil the prominent role of interbasin interaction, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these interbasin contrasts. Our statistical analyses suggest that these tropical interbasin contrasts could serve as a more reliable indicator for late-spring precipitation anomalies than El Niño–Southern Oscillation. The conclusions are reinforced by analyses of seven climate forecasting systems within the North American Multi-Model Ensemble, offering an optimistic outlook for enhancing real-time forecasting of late-spring precipitation in the southern plains. However, the current predictive skills of the interbasin contrasts across the prediction systems are hindered by the lower predictability of the tropical Atlantic Ocean, pointing to the need for future research to refine climate prediction models further. Significance Statement Agriculture and infrastructure in the southern plains face challenges from severe late-spring precipitation extremes. Traditional predictors like El Niño–Southern Oscillation (ENSO) lose effectiveness during the critical spring-to-summer transition, creating a forecasting gap. This study introduces the concept of tropical interbasin interactions, known to enhance seasonal predictability for late-spring precipitation in the southern plains. Novel climate model experiments highlight contributions from the tropical Pacific and Atlantic, offering a promising predictability that potentially surpasses the limitations of ENSO-based predictions. These outcomes hold the potential for developing operational forecasts of late-spring precipitation anomalies in the southern plains, enabling proactive risk management.

Chikamoto, Yoshimitsu↗

NASA NTRS2000

Future Weather Forecasting in the Year 2020-Investing in Technology Today: Improving Weather and Environmental Predictions

Fast-forward twenty years to the nightly simultaneous TV/webcast. Accurate 8-14 day regional forecasts will be available as will be a whole host of linked products including economic impact, travel, energy usage, etc. On-demand, personalized street-level forecasts will be downloaded into your PDA. Your home system will automatically update the products of interest to you (e.g. severe storm forecasts, hurricane predictions, etc). Short and long range climate forecasts will be used by your "Quicken 2020" to make suggest changes in your "futures" investment portfolio. Through a lively and informative multi-media presentation, leading Space-Earth Science Researchers and Technologists will share their vision for the year 2020, offering a possible futuristic forecast enabled through the application of new technologies under development today. Copies of the 'broadcast' will be available on Beta Tape for your own future use. If sufficient interest exists, the program may also be made available for broadcasters wishing to do stand-ups with roll-ins from the San Francisco meeting for their viewers back home.

Anthes, Richard↗

NASA NTRS2021

Precipitable water vapor over oceans from the Maritime Aerosol Network: Evaluation of global models and satellite products under clear sky conditions

We present results from an evaluation of precipitable water vapor (W) over remote oceanic areas as derived from global reanalysis models and from satellites against observations from the Maritime Aerosol Network (MAN) for cloudless skies during the period of 2004–2017. They cover polar, mid latitude and tropical oceanic regions and represent a first effort to use MAN observations for such evaluation. The global reanalysis model products evaluated in this study are from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA I), and the Climate Forecast System Reanalysis (CFSR) model. The satellite products evaluated are from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Polarization and Directionality of the Earth's Reflectances (POLDER), the Global Ozone Monitoring Experiment (GOME-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and the Atmospheric Infra-red Sounder (AIRS). Satellite retrievals of W are based on the attenuation of solar reflected light by water vapor absorption bands, except those from AIRS that rely on brightness temperature measurements. A very good agreement is observed between the model estimates and MAN, with mean differences of ~5% and standard deviations of ~15%. These results are within the uncertainties associated with the models and the measurements, indicating the skill of the reanalysis models to estimate W over oceans under clear sky conditions. Mean differences of W between the satellite and MAN products are ~11, 6.7, 12, −7, and 3% for MODIS, POLDER, GOME-2, SCIAMACHY and AIRS respectively, while their standard deviations are 31, 29, 28, 20 and 17%. These differences reveal the need to address inconsistencies among different satellite sensors and ground-based measurements to reduce the uncertainties associated with the retrievals.

Daniel Pérez-Ramírez↗

NASA NTRSDate not supplied

Importance of Radiative Transfer Models in Atmospheric Remote Sensing

Radiative transfer models (RTMs) play a significant role in the development of satellite instruments for remote sensing applications. These models simulate electromagnetic radiation's propagation through the atmosphere, providing valuable insights into atmosphere-radiation interactions. RTMs facilitate the optimization of satellite instrument designs, ensuring their ability to measure targeted atmospheric and surface properties accurately. Moreover, they aid in simulating instrument’s measurements under various atmospheric conditions, enabling calibration and validation processes to enhance data quality and reliability. RTMs are extensively used in the Observing System Simulation Experiments (OSSE), to generate synthetic observations. By incorporating RTMs into OSSE, we can assess the potential impact of future satellite missions, sensor configurations, and data assimilation techniques. This approach allows for the optimization of satellite instruments and constellations and the evaluation of their influence on weather forecasting, climate monitoring, and other Earth science applications. Another crucial application area of RT models is data assimilation, where they play a fundamental role in combining satellite observations with numerical models to improve atmospheric and environmental predictions. RTMs provide the link between observed radiances and atmospheric parameters, enhancing the accuracy of numerical models and generating more reliable forecasts for weather events, air quality assessments, and climate projections. Moreover, adapting RT models to capture the intricate radiation interactions within the Planetary Boundary Layer will significantly contribute to improving weather forecasting and climate change projections. Current community radiative transfer (RT) models are primarily developed and optimized for operational data assimilation of satellite observations. These models excel at assimilating satellite data into numerical weather prediction models to improve forecast accuracy. However, their focus on data assimilation limits their suitability for other important applications, such as satellite instrument development, OSSE, and Planetary Boundary Layer (PBL) studies. Moreover, for PBL studies, RT models need to be adapted to capture the intricate radiation interactions within this crucial atmospheric layer. Developing RT models that can represent the PBL's unique characteristics, such as surface interactions, will contribute significantly to understanding and predicting weather phenomena, air quality, and climate dynamics. This abstract provides a comprehensive overview of the current status of RT models and highlights their limitations concerning satellite instrument development, OSSE, and PBL studies. Addressing these shortcomings requires concerted efforts to enhance RT models' capabilities and expand their applications beyond data assimilation. By investing in research and development to improve these

Isaac Moradi↗

NASA NTRSDate not supplied

A Procedure for Obtaining Forecast Turbulence Parameters in the Atmospheric Boundary Layer for Acoustic Propagation

Propagation through turbulence causes a mean reduction in loudness of sonic booms and in some cases can cause significant fluctuations in loudness about that mean. Understanding these effects is important for accurately planning loudness levels during upcoming community noise tests with the X-59 aircraft as well as mitigating the risk for excessive loudness due to turbulence. Current methods for modeling acoustic propagation through turbulence require several atmospheric parameters including the atmospheric boundary layer (ABL) height, friction velocity, mixed-layer velocity scale, surface-layer temperature scale, as well as ambient pressure, temperature, and humidity within the ABL. Accurate forecasts of these parameters are needed for X-59 flight planning and may also be useful for planning future supersonic aircraft certification flight tests. This presentation showcases one method for obtaining the forecast turbulence and ambient atmospheric parameters from freely available Climate Forecast System Version 2 data. Forecast parameters are compared to measurements to assess accuracy and utility.

sonic boom↗

NASA NTRS2013

The Relation Between Atmospheric Humidity and Temperature Trends for Stratospheric Water

We analyze the relation between atmospheric temperature and water vapor-a fundamental component of the global climate system-for stratospheric water vapor (SWV). We compare measurements of SWV (and methane where available) over the period 1980-2011 from NOAA balloon-borne frostpoint hygrometer (NOAA-FPH), SAGE II, Halogen Occultation Experiment (HALOE), Microwave Limb Sounder (MLS)/Aura, and Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) to model predictions based on troposphere-to-stratosphere transport from ERA-Interim, and temperatures from ERA-Interim, Modern Era Retrospective-Analysis (MERRA), Climate Forecast System Reanalysis (CFSR), Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC), HadAT2, and RICHv1.5. All model predictions are dry biased. The interannual anomalies of the model predictions show periods of fairly regular oscillations, alternating with more quiescent periods and a few large-amplitude oscillations. They all agree well (correlation coefficients 0.9 and larger) with observations for higherfrequency variations (periods up to 2-3 years). Differences between SWV observations, and temperature data, respectively, render analysis of the model minus observation residual difficult. However, we find fairly well-defined periods of drifts in the residuals. For the 1980s, model predictions differ most, and only the calculation with ERA-Interim temperatures is roughly within observational uncertainties. All model predictions show a drying relative to HALOE in the 1990s, followed by a moistening in the early 2000s. Drifts to NOAA-FPH are similar (but stronger), whereas no drift is present against SAGE II. As a result, the model calculations have a less pronounced drop in SWV in 2000 than HALOE. From the mid-2000s onward, models and observations agree reasonably, and some differences can be traced to problems in the temperature data. These results indicate that both SWV and temperature data may still suffer from artifacts that need to be resolved in order to answer the question whether the large-scale flow and temperature field is sufficient to explain water entering the stratosphere.

Fueglistaler, S.↗

NASA NTRS2013

Advancing Drought Understanding, Monitoring and Prediction

Having the capacity to monitor droughts in near-real time and providing accurate drought prediction from weeks to seasons in advance can greatly reduce the severity of social and economic damage caused by drought, a leading natural hazard for North America. The congressional mandate to establish the National Integrated Drought Information System (NIDIS; Public Law 109-430) in 2006 was a major impulse to develop, integrate, and provide drought information to meet the challenges posed by this hazard. Significant progress has been made on many fronts. On the research front, efforts by the broad scientific community have resulted in improved understanding of North American droughts and improved monitoring and forecasting tools. We now have a better understanding of the droughts of the twentieth century including the 1930s "Dust Bowl"; we have developed a broader array of tools and datasets that enhance the official North American Drought Monitor based on different methodologies such as state-of-the-art land surface modeling (e.g., the North American Land Data Assimilation System) and remote sensing (e.g., the evaporative stress index) to better characterize the occurrence and severity of drought in its multiple manifestations. In addition, we have new tools for drought prediction [including the new National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2, for operational prediction and an experimental National Multimodel Ensemble] and have explored diverse methodologies including ensemble hydrologic prediction approaches. Broad NIDIS-inspired progress is influencing the development of a Global Drought Information System (GDIS) under the auspices of the World Climate Research Program. Despite these advances, current drought monitoring and forecasting capabilities still fall short of users' needs, especially the need for skillful and reliable drought forecasts at regional and local scales. To tackle this outstanding challenging problem, focused and coordinated research efforts are needed, drawing from excellence across the broad drought research community. To meet this challenge, National Oceanic and Atmospheric Administration (NOAA)'s Drought Task Force was established in October 2011 with the ambitious goal of achieving significant new advances in the ability to understand, monitor, and predict drought over North America. The Task Force (duration of October 2011-September 2014) is an initiative of NOAA's Climate Program Office Modeling, Analysis, Predictions, and Projections (MAPP) program in partnership with NIDIS. It brings together over 30 leading MAPP-funded drought scientists from multiple academic and federal institutions [involves scientists from NOAA's research laboratories and centers, the National Aeronautics and Space Administration (NASA), U.S. Department of Agriculture, National Center for Atmospheric Research (NCAR), and many universities] in a concerted research effort that builds on individual MAPP research projects. These projects span the wide spectrum of drought research needed to make fundamental advances, from those aimed at the basic understanding of drought mechanisms to those aimed at testing new drought monitoring and prediction tools for operational and service purposes (as part of NCEP's Climate Test Bed). The Drought Task Force provides focus and coordination to MAPP drought research activities and also facilitates synergies with other national and international drought research efforts, including those by the GDIS.

GDIS↗

NASA NTRS2024

Investigating the Strength and Variability of El Niño Southern Oscillation Teleconnections to Hydroclimate and Maize Yields in Southern and East Africa

The state of the El Niño Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including Sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts.

El Niño Southern Oscillation↗

NASA NTRS1990

Simulated forecast error and climate drift resulting from the omission of the upper stratosphere in numerical models

Using an NCAR community climate model, Version I, the forecast error growth and the climate drift resulting from the omission of the upper stratosphere are investigated. In the experiment, the control simulation is a seasonal integration of a medium horizontal general circulation model with 30 levels extending from the surface to the upper mesosphere, while the main experiment uses an identical model, except that only the bottom 15 levels (below 10 mb) are retained. It is shown that both random and systematic errors develop rapidly in the lower stratosphere with some local propagation into the troposphere in the 10-30-day time range. The random growth rate in the troposphere in the case of the altered upper boundary was found to be slightly faster than that for the initial-condition uncertainty alone. However, this is not likely to make a significant impact in operational forecast models, because the initial-condition uncertainty is very large.

Boville, Byron A.↗

DOE OSTI2025

Machine learning methods for weather forecasting

SAND2025-14466O This repository contains code for developing, training, and evaluating machine learning models for weather and climate forecasting, including forecast skill assessment, feature importance analysis, and reproducible workflows for model comparison. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Holthuijzen, Maike [Sandia National Lab. (SNL-CA),↗

NASA NTRS2008

Application of the NASA A-Train to Evaluate Clouds Simulated by the Weather Research and Forecast Model

The CloudSat Mission, part of the NASA A-Train, is providing the first global survey of cloud profiles and cloud physical properties, observing seasonal and geographical variations that are pertinent to evaluating the way clouds are parameterized in weather and climate forecast models. CloudSat measures the vertical structure of clouds and precipitation from space through the Cloud Profiling Radar (CPR), a 94 GHz nadir-looking radar measuring the power backscattered by clouds as a function of distance from the radar. One of the goals of the CloudSat mission is to evaluate the representation of clouds in forecast models, thereby contributing to improved predictions of weather, climate and the cloud-climate feedback problem. This paper highlights potential limitations in cloud microphysical schemes currently employed in the Weather Research and Forecast (WRF) modeling system. The horizontal and vertical structure of explicitly simulated cloud fields produced by the WRF model at 4-km resolution are being evaluated using CloudSat observations in concert with products derived from MODIS and AIRS. A radiative transfer model is used to produce simulated profiles of radar reflectivity given WRF input profiles of hydrometeor mixing ratios and ambient atmospheric conditions. The preliminary results presented in the paper will compare simulated and observed reflectivity fields corresponding to horizontal and vertical cloud structures associated with midlatitude cyclone events.

Molthan, Andrew L.↗

NASA NTRS2020

Unknowability in Climate Science: Chaos, Structure, and Society

The limits to knowledge in climate forecasts arise from three mechanisms. One is inherent to the complex nonlinearity of the climate system, one is related to inherent imperfection of models in representing the real world, and one is a reflection on the coupling of our knowledge of potential future states and real-world actions that may be taken to avoid some subset of them. I refer to these limits as chaos, structure, and society. I provide background on how each adds to the unknowability of climate, and demonstrate that this unknowability is limited; not all aspects of past and future climates are unknowable.

Climate science↗