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NASA NTRS2019

The Ability of GeoCarb to Constrain the Interannual Variability of Carbon Gases over the Amazon

We perform a number of idealized assimilation experiments with the GEOS constituent data assimilation system to test the ability of GeoCarb retrievals of CO, CO2, and CH4 to constrain the interannual variability of these gases over the Amazon. Retrievals for instruments on other satellites which observe in similar channels (e.g. MOPITT, GOSAT, and OCO-2) are limited due to persistent cloud coverage. Given its ability to sample the same location multiple times in one day, the expectation is that GeoCarb retrievals will return more soundings than those from previous missions. The goal of the assimilation experiments is to understand which scanning strategies lead to the best sounding densities and thus have the best chance of constraining interannual variability in the carbon species. The experiments each begin by picking a given year at random from a nature run (i.e., a model simulation meant to represent the truth). The model fields are sampled according to a given strategy and then screened to account for cloud coverage. Next, we pick another year at random and assimilate the synthetic GeoCarb samples into the GEOS model for that year. The output of the assimilation, 6-hourly, 3D fields of each constituent, is then directly comparable to the nature run. This comparison allows us to evaluate the ability of GeoCarb measurements to constrain the interannual variability of each gas.

Weir, B.↗

NASA NTRS1986

Remote sensing of cloud distributions over the Bayanhar Mountains - Watershed of the Yangtze and Yellow rivers

Although the two largest rivers in China originate in the same region separated only by the Bayanhar Mountains as a watershed, the Yangtze and Yellow rivers behave in quite different ways. Most of the warm and humid air currents from the Arabian sea and the Bay of Bengal are blocked by the Bayanhar Mountains. As a result the amount of water in the Yellow River is only 5 percent of that in the Yangtze river. Based on the cloud coverage area and the cloud volumetric distributions, and also the thickness above 9.4 km of the cumulus clouds located north and south of the Bayanhar Mountains from the geosynchronous satellite infrared imagery, the results suggest that a more detailed investigation is warranted in the hope that the proper modification of cumuli north of the Bayanhar Mountains would enhance the rainfall over the fountainhead of the Yellow River.

Hung, R. J.↗

NASA NTRS1990

Satellite remote sensing of water resources in the Yangtze and Yellow Rivers of China based on infrared imagery of cloud distributions

Although the two largest rivers in China originate in the same region, separated only by the Bayanhar Mountains as a watershed, the Yangtze and Yellow Rivers behave in quite different ways. Most of the warm and humid air currents from the Arabian Sea and Bay of Bengal are blocked by the Bayanhar Mountains. As a result, the amount of water in the Yellow River is only 5 percent of that in the Yangtze River. Based on the cloud coverage area and the cloud volumetric distributions, and also the thickness above 9.4 kms of the cumulus clouds located north and south of the Bayanhar Mountains, from GEO satellite IR imagery, the results suggest that a more detailed investigation is warranted in the hope that the proper modification of cumuli north of the Bayanhar Mountains would enhance the rainfall over the fountainhead of the Yellow River.

Hung, R. J.↗

NASA NTRS2012

A Robust Multi-Scale Modeling System for the Study of Cloud and Precipitation Processes

During the past decade, numerical weather and global non-hydrostatic models have started using more complex microphysical schemes originally developed for high resolution cloud resolving models (CRMs) with 1-2 km or less horizontal resolutions. These microphysical schemes affect the dynamic through the release of latent heat (buoyancy loading and pressure gradient) the radiation through the cloud coverage (vertical distribution of cloud species), and surface processes through rainfall (both amount and intensity). Recently, several major improvements of ice microphysical processes (or schemes) have been developed for cloud-resolving model (Goddard Cumulus Ensemble, GCE, model) and regional scale (Weather Research and Forecast, WRF) model. These improvements include an improved 3-ICE (cloud ice, snow and graupel) scheme (Lang et al. 2010); a 4-ICE (cloud ice, snow, graupel and hail) scheme and a spectral bin microphysics scheme and two different two-moment microphysics schemes. The performance of these schemes has been evaluated by using observational data from TRMM and other major field campaigns. In this talk, we will present the high-resolution (1 km) GeE and WRF model simulations and compared the simulated model results with observation from recent field campaigns [i.e., midlatitude continental spring season (MC3E; 2010), high latitude cold-season (C3VP, 2007; GCPEx, 2012), and tropical oceanic (TWP-ICE, 2006)].

Tao, Wei-Kuo↗

NASA NTRS1994

The interpretation of remotely sensed cloud properties from a model paramterization perspective

A study has been made of the relationship between mean cloud radiative properties and cloud fraction in stratocumulus cloud systems. The analysis is of several Land Resources Satellite System (LANDSAT) images and three hourly International Satellite Cloud Climatology Project (ISCCP) C-1 data during daylight hours for two grid boxes covering an area typical of a general circulation model (GCM) grid increment. Cloud properties were inferred from the LANDSAT images using two thresholds and several pixel resolutions ranging from roughly 0.0625 km to 8 km. At the finest resolution, the analysis shows that mean cloud optical depth (or liquid water path) increases somewhat with increasing cloud fraction up to 20% cloud coverage. More striking, however, is the lack of correlation between the two quantities for cloud fractions between roughly 0.2 and 0.8. When the scene is essentially overcast, the mean cloud optical tends to be higher. Coarse resolution LANDSAT analysis and the ISCCP 8-km data show lack of correlation between mean cloud optical depth and cloud fraction for coverage less than about 90%. This study shows that there is perhaps a local mean liquid water path (LWP) associated with partly cloudy areas of stratocumulus clouds. A method has been suggested to use this property to construct the cloud fraction paramterization in a GCM when the model computes a grid-box-mean LWP.

HARSHVARDHAN↗

NASA NTRS2002

Science Objectives of EOS-Aura's Ozone Monitoring Instrument (OMI)

OMI is a UV/VIS nadir solar backscatter spectrograph, which provides near global coverage in one day with a spatial resolution of 13 x 24 sq km. OMI is a new instrument, with a heritage from the European satellite instruments GOME, GOMOS and SCIAMACHY. OMI's unique capabilities for measuring important trace gases with a small footprint and daily global coverage, in conjunction with the other Aura instruments, will make a major contribution to our understanding of stratospheric and tropospheric chemistry and climate change. OMI will measure solar irradiance and Earth radiances in the wavelength range of 270 to 500 nm with spectral resolution of about 0.5 nm and a spectral sampling of about 2-3 per FWHM. From these observations, total columns of O3, NO2, BrO and SO2 will be derived from the back-scattered solar radiance using differential absorption spectroscopy (DOAS). The TOMS total ozone record will also be continued by employing the well established TOMS algorithm. Because of the high accuracy and spatial resolution of the measurements, a good estimate of tropospheric amounts of ozone and NO2 are expected. Ozone profiles will be derived using the optimal estimation method. The spectral aerosol optical depth will be determined from measurements between 340 and 500 nm. This will provide information on aerosol concentration, aerosol size distribution and aerosol type. This wavelength range makes it possible to retrieve aerosol information over both land and sea. OMI observations will also allow retrievals of cloud coverage and cloud heights. From these products, the UV-B flux at the surface can then be derived with high spatial resolution.

Levelt, P. F.↗

NASA NTRS2001

Predictability of the California Current System

The physical and biological oceanography of the Southern California Bight (SCB), a highly productive subregion of the California Current System (CCS) that extends from Point Conception, California, south to Ensenada, Mexico, continues to be extensively studied. For example, the California Cooperative Oceanic Fisheries Investigations (CalCOFI) program has sampled this region for over 50 years, providing an unparalleled time series of physical and biological data. However, our understanding of what physical processes control the large-scale and mesoscale variations in these properties is incomplete. In particular, the non-synoptic and relatively coarse spatial sampling (70km) of the hydrographic grid does not completely resolve the mesoscale eddy field (Figure 1a). Moreover, these unresolved physical variations exert a dominant influence on the evolution of the ecosystem. In recent years, additional datasets that partially sample the SCB have become available. Acoustic Doppler Current Profiler (ADCP) measurements, which now sample upper-ocean velocity between stations, and sea level observations along TOPEX tracks give a more complete picture of the mesoscale variability. However, both TOPEX and ADCP are well-sampled only along the cruise or orbit tracks and coarsely sampled in time and between tracks. Surface Lagrangian drifters also sample the region, although irregularly in time and space. SeaWiFS provides estimates of upper-ocean chlorophyll-a (chl-alpha), usually giving nearly complete coverage for week-long intervals, depending on cloud coverage. Historical ocean color data from the Coastal Zone Color Scanner (CZCS) has been used extensively to determine phytoplankton patterns and variability, characterize the primary production across the SCB coastal fronts, and describe the seasonal and interannual variability in pigment concentrations. As in CalCOFI, these studies described much of the observed structures and their variability over relatively large space and time scales.

Miller, Arthur J.↗

NASA NTRS1991

The effect of clouds on the earth's radiation budget

The radiative fluxes from the Earth Radiation Budget Experiment (ERBE) and the cloud properties from the International Satellite Cloud Climatology Project (ISCCP) over Indonesia for the months of June and July of 1985 and 1986 were analyzed to determine the cloud sensitivity coefficients. The method involved a linear least squares regression between co-incident flux and cloud coverage measurements. The calculated slope is identified as the cloud sensitivity. It was found that the correlations between the total cloud fraction and radiation parameters were modest. However, correlations between cloud fraction and IR flux were improved by separating clouds by height. Likewise, correlations between the visible flux and cloud fractions were improved by distinguishing clouds based on optical depth. Calculating correlations between the net fluxes and either height or optical depth segregated cloud fractions were somewhat improved. When clouds were classified in terms of their height and optical depth, correlations among all the radiation components were improved. Mean cloud sensitivities based on the regression of radiative fluxes against height and optical depth separated cloud types are presented. Results are compared to a one-dimensional radiation model with a simple cloud parameterization scheme.

Ziskin, Daniel↗

NASA NTRS2015

Understanding the Atmosphere of 51 Eri b: Do Photochemical Hazes Cloud the Planets Spectrum?

The first young giant planet to be discovered by the Gemini Planet Imager was the (is) approximately 2MJ planet 51 Eri b. This approximately 20 Myr old young Jupiter is the first directly imaged planet to show unmistakable methane in H band. To constrain the planet's mass, atmospheric temperature, and composition, the GPI J and H band spectra as well as some limited photometric points were compared to the predictions of substellar atmosphere models. The best fitting models reported in the discovery paper (Macintosh et al. 2015) relied upon a combination of clear and cloudy atmospheric columns to reproduce the data. However for an object as cool as 700 K, the origin of the cloud coverage is somewhat puzzling, as the global silicate and iron clouds would be expected to have sunk well below the photosphere by this effective temperature. While strong vertical mixing in these low gravity atmospheres remains a plausible explanation, we have explored whether atmospheric photochemistry, driven by the UV flux from the primary star, may yield hazes that also influence the observed spectrum of the planet. To explore this possibility we have modeled the atmospheric photochemistry of 51 Eri b using two state-of-the-art photochemical models, both capable of predicting yields of complex hydrocarbons under various atmospheric conditions. In our presentation we will summarize the modeling approach employed to characterize 51 Eri b, explaining constraints on the planet's effective temperature, gravity, and atmospheric composition and also present results of our studies of atmospheric photochemistry. We will discuss whether photochemical hazes could indeed be responsible for the particulate opacity that apparently sculpts the spectrum of the planet.

GPI - Gemini Planet Imager↗

NASA NTRS2015

How Difficult is it to Reduce Low-Level Cloud Biases With the Higher-Order Turbulence Closure Approach in Climate Models?

Low-level clouds cover nearly half of the Earth and play a critical role in regulating the energy and hydrological cycle. Despite the fact that a great effort has been put to advance the modeling and observational capability in recent years, low-level clouds remains one of the largest uncertainties in the projection of future climate change. Low-level cloud feedbacks dominate the uncertainty in the total cloud feedback in climate sensitivity and projection studies. These clouds are notoriously difficult to simulate in climate models due to its complicated interactions with aerosols, cloud microphysics, boundary-layer turbulence and cloud dynamics. The biases in both low cloud coverage/water content and cloud radiative effects (CREs) remain large. A simultaneous reduction in both cloud and CRE biases remains elusive. This presentation first reviews the effort of implementing the higher-order turbulence closure (HOC) approach to representing subgrid-scale turbulence and low-level cloud processes in climate models. There are two HOCs that have been implemented in climate models. They differ in how many three-order moments are used. The CLUBB are implemented in both CAM5 and GDFL models, which are compared with IPHOC that is implemented in CAM5 by our group. IPHOC uses three third-order moments while CLUBB only uses one third-order moment while both use a joint double-Gaussian distribution to represent the subgrid-scale variability. Despite that HOC is more physically consistent and produces more realistic low-cloud geographic distributions and transitions between cumulus and stratocumulus regimes, GCMs with traditional cloud parameterizations outperform in CREs because tuning of this type of models is more extensively performed than those with HOCs. We perform several tuning experiments with CAM5 implemented with IPHOC in an attempt to produce the nearly balanced global radiative budgets without deteriorating the low-cloud simulation. One of the issues in CAM5-IPHOC is that cloud water content is much higher than in CAM5, which is combined with higher low-cloud coverage to produce larger shortwave CREs in some low-cloud prevailing regions. Thus, the cloud-radiative feedbacks are exaggerated there. The turning exercise is focused on microphysical parameters, which are also commonly used for tuning in climate models. The results will be discussed in this presentation.

Xu, Kuan-Man↗

DOE OSTI2022

Evaluation of Model Summertime Boundary Layer Cloud Development over Complex Terrain in New York State

Weather forecasting over complex terrain with diverse land cover is challenging. Utilizing the high-resolution observations from New York State Mesonet (NYSM), we are able to evaluate the surface processes of the Weather Research Forecast (WRF) Model in a detailed, scale-dependent manner. Here, in the study, possible impacts of land–atmosphere interaction on surface meteorology and boundary layer cloud development are investigated with different model resolutions, land surface models (LSMs), and planetary boundary layer (PBL) physical parameterizations. The High-Resolution Rapid Refresh, version 3 (HRRR), forecasting model is used as a reference for the sensitivity evaluation. Results show that over complex terrain, the high-resolution simulations (1 km × 60 vertical levels) generally perform better compared to low-resolution (3 km × 50 levels) in both surface meteorology and cloud fields. LSMs play a more important role in surface meteorology compared to PBL schemes. The NoahMP land surface model exhibits daytime warmer and drier biases compared to the Rapid Update Cycle (RUC) due to better prediction of the Bowen ratio in RUC. The PBL schemes would affect the convective strength in the boundary layer. The Shin–Hong (SH) scale-aware scheme tends to produce the strongest convective strength in the PBL, while the ACM2 PBL scheme rarely resolved convection even at 1-km resolution. By considering the radiation effect of subgrid-scale (SGS) clouds, the Mellor–Yamada–Nakanishi–Niino eddy diffusivity mass flux (MYNN-EDMF) predicted the highest cloud coverage and lowest surface solar radiation bias. The configuration of SGS clouds in MYNN-EDMF would not only significantly reduce shortwave radiation bias, but also affect the convection behaviors through land surface–cloud–radiation interaction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Rainbows and Climate Change: A tutorial on climate model diagnostics and parameterization

Earth System Models (ESMs) must represent processes below the grid scale of a model using representations (parameterizations) of physical and chemical processes. As a tutorial exercise to understand diagnostics and parameterization, this work presents a representation of rainbows for an ESM: the Community Earth System Model version 2 (CESM2). Using the 'state' of the model, basic physical laws, and some assumptions, we generate a representation of this unique optical phenomena as a diagnostic output. Rainbow occurrence and it's possible changes are related to cloud occurrence and rain formation which are critical uncertainties for climate change prediction. The work highlights issues which are typical of many diagnostics parameterizations such as assumptions, uncertain parameters and the difficulty of evaluation against uncertain observations. Results agree qualitatively with limited available global 'observations' of Rainbows. Rainbows are seen in expected locations in the sub-tropics over the ocean where broken clouds and frequent precipitation occurs. The diurnal peak is in the morning over ocean and in the evening over land. The representation of rainbows is found to be quantitatively sensitive to the assumed amount of cloudiness and the amount of stratiform rain. Rainbows are projected to have decreased, mostly in the Northern Hemisphere, due to aerosol pollution effects increasing cloud coverage since 1850. In the future, continued climate change is projected to decrease cloud cover, associated with a positive cloud feedback. As a result the rainbow diagnostic projects that rainbows will increase in the future, with the largest changes at mid-latitudes. The diagnostic may be useful for assessing cloud parameterizations, and is an exercise in how to build and test parameterizations of atmospheric phenomena.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1987

The application of time series models to cloud field morphology analysis

A modeling method for the quantitative description of remotely sensed cloud field images is presented. A two-dimensional texture modeling scheme based on one-dimensional time series procedures is adopted for this purpose. The time series procedure used is the seasonal autoregressive, moving average (ARMA) process in Box and Jenkins. Cloud field properties such as directionality, clustering and cloud coverage can be retrieved by this method. It has been demonstrated that a cloud field image can be quantitatively defined by a small set of parameters and synthesized surrogates can be reconstructed from these model parameters. This method enables cloud climatology to be studied quantitatively.

Chin, Roland T.↗

NASA NTRS2018

Quantifying the Early Snowmelt Event of 2015 in the Cascade Mountains, USA by Developing and Validating MODIS-Based Snowmelt Timing Maps

Spring snowmelt serves as the major hydrological contribution to many watersheds of the US West. Since the 1970s the conterminous western USA has seen an earlier arrival of spring snowmelt. The extremely low snowpack and early melt of 2015 in the Cascade Mountains may be a harbinger of winters to come, underscoring the interest in advancements in spring snowmelt monitoring. Target-of-opportunity and point measurements of snowmelt using meteorological stations or stream gauges are common sources of these data, however, there have been few attempts to identify snowmelt timing using remote sensing. In this study, we describe the creation of snowmelt timing maps (STMs) which identify the day of year that each pixel of a remotely sensed image transitions from "snow-covered" to "no snow" during the spring melt season, controlling for cloud coverage and ephemeral spring snow storms. Derived from the 500 meter MODerate-resolution Imaging Spectroradiometer (MODIS) standard snow map, MOD10A2, this new dataset provides annual maps of snowmelt timing, with corresponding maps of cloud interference and interannual variability in snow coverage from 2001-2015. We first show that the STMs agree strongly with in-situ snow telemetry (SNOTEL) meteorological station measurements in terms of snowmelt timing. We then use the STMs to investigate the early snowmelt event of 2015 in the Cascade Mountains, USA, highlighting the protected areas of Mt. Rainier, Crater Lake, and Lassen Volcanic National Parks. In 2015 the Cascade Mountains experienced snowmelt 41 days earlier than the 2001-2015 average, with 25 percent of its land area melting more than 65 days earlier than average. The upper elevations of the Cascade Mountains experienced the greatest snowmelt anomaly. Our results are relevant to land managers and biologists as they plan adaptation strategies for mitigating the effects of climate change throughout temperate mountains.

snowmelt timing↗

NASA NTRS1977

Application of LANDSAT system for improving methodology for inventory and classification of wetlands

The author has identified the following significant results. Processing of LANDSAT MSS data for detection of prairie ponds and lakes was completed. Data coverage included a 36,876 sq km area in southeastern North Dakota during May and July. Cloud coverage limited the May coverage to 87% of the total area. Data analysis was accomplished using three software programs. Wetland identification by MSS sensors were compared to visual counts obtained by observers in low flying aircraft. Pond numbers identified by LANDSAT averaged about 20% of those counted visually. The discrepancy was attributed to the fact that approximately 75% of the ponds in the glaciated prairie region are less than 0.4 ha in size. It is significant, however, that LANDSAT counts accurately reflect trends.

Gilmer, D. S.↗

NASA NTRS1994

Global shortwave energy budget at the earth's surface from ERBE observations

A method is proposed to compute the net solar (shortwave) irradiance at the earth's surface from Earth Radiation Budget Experiment (ERBE) data in the S4 format. The S4 data are monthly averaged broadband planetary albedo collected at selected times during the day. Net surface shortwave irradiance is obtained from the shortwave irradiance incident at the top of the atmosphere (known) by subtracting both the shortwave energy flux reflected by the earth-atmosphere system (measured) and the energy flux absorbed by the atmosphere (modeled). Precalculated atmospheric- and surface-dependent functions that characterize scattering and absorption in the atmosphere are used, which makes the method easily applicable and computationally efficient. Four surface types are distinguished, namely, ocean, vegetation, desert, and snow/ice. Over the tropical Pacific Ocean, the estimates based on ERBE data compare well with those obtained from International Satellite Cloud Climatology Project (ISCCP) B3 data. For the 9 months analyzed the linear correlation coefficient and the standard difference between the two datasets are 0.95 and 14 W/sq m (about 6% of the average shortwave irradiance), respectively, and the bias is 15 W/sq m (higher ERBE values). The bias, a strong function of ISCCP satellite viewing zenith angle, is mostly in the ISCCP-based estimates. Over snow/ice, vegetation, and desert no comparison is made with other satellite-based estimates, but theoretical calculations using the discrete ordinate method suggest that over highly reflective surfaces (snow/ice, desert) the model, which accounts crudely for multiple reflection between the surface and clouds, may substantially overestimate the absorbed solar energy flux at the surface, especially when clouds are optically thick. The monthly surface shortwave irradiance fields produced for 1986 exhibit the main features characteristic of the earth's climate. As found in other studies, our values are generally higher than Esbensen and Kushnir's by as much as 80 W/sq m in the tropical oceans. A cloud parameter, defined as the difference between clear-sky and actual irradiances normalized to top-of-atmosphere clear-sky irradiance, is also examined. This parameter, minimally affected by sun zenith angle, is higher in the midlatitude regions of storm tracks than in the intertropical convergence zone (ITCZ), suggesting that, on average, the higher cloud coverage in midlatitudes is more effective at reducing surface shortwave irradiance than opaque, convective, yet sparser clouds in the ITCZ. Surface albedo estimates are realistic, generally not exceeding 0.06 in the ocean, as high as 0.9 in polar regions, and reaching 0.5 in the Sahara and Arabian deserts.

Breon, Francois-Marie↗

NASA NTRS1986

Determination of cloud fields from analysis of HIRS2/MSU sounding data

IR and microwave remote sensing data collected with the HIRS2 and MSU sensors on the NOAA polar-orbiting satellites were evaluated for their effectiveness as bases for determining the cloud cover and cloud physical characteristics. Techniques employed to adjust for day-night alterations in the radiance fields are described, along with computational procedures applied to compare scene pixel values with reference values for clear skies. Sample results are provided for the mean cloud coverage detected over South America and Africa June 1979, with attention given to concurrent surface pressure and cloud top pressure values.

Susskind, J.↗

NASA NTRS2005

Improvements in Near-Terminator and Nocturnal Cloud Masks using Satellite Image Data over the Atmospheric Radiation Measurement Sites

Cloud detection using satellite measurements presents a big challenge near the terminator where the visible (VIS; 0.65 (micro)m) channel becomes less reliable and the reflected solar component of the solar infrared 3.9-(micro)m channel reaches very low signal-to-noise ratio levels. As a result, clouds are underestimated near the terminator and at night over land and ocean in previous Atmospheric Radiation Measurement (ARM) Program cloud retrievals using Geostationary Operational Environmental Satellite (GOES) imager data. Cloud detection near the terminator has always been a challenge. For example, comparisons between the CLAVR-x (Clouds from Advanced Very High Resolution Radiometer (AVHRR)) cloud coverage and Geoscience Laser Altimeter System (GLAS) measurements north of 60 degrees N indicate significant amounts of missing clouds from AVHRR because this part of the world was near the day/night terminator viewed by AVHRR. Comparisons between MODIS cloud products and GLAS at the same regions also shows the same difficulty in the MODIS cloud retrieval (Pavolonis and Heidinger 2005). Consistent detection of clouds at all times of day is needed to provide reliable cloud and radiation products for ARM and other research efforts involving the modeling of clouds and their interaction with the radiation budget. To minimize inconsistencies between daytime and nighttime retrievals, this paper develops an improved twilight and nighttime cloud mask using GOES-9, 10, and 12 imager data over the ARM sites and the continental United States (CONUS).

Detection↗