Cooperative Institute for Meteorological Satellite Studies

Abstract. We describe and begin to evaluate a parameterization to include the vertical transport of hot gases and particles emitted from biomass burning in low resolution atmospheric-chemistry transport models. This sub-grid transport mechanism is simulated by embedding a 1-D cloud-resolving model with appropriate lower boundary conditions in each column of the 3-D host model. Through assimilation of remote sensing fire products, we recognize which columns have fires. Using a land use dataset appropriate fire properties are selected. The host model provides the environmental conditions, allowing the plume rise to be simulated explicitly. The derived height of the plume is then used in the source emission field of the host model to determine the effective injection height, releasing the material emitted during the flaming phase at this height. Model results are compared with CO aircraft profiles from an Amazon basin field campaign and with satellite data, showing the huge impact that this mechanism has on model performance. We also show the relative role of each main vertical transport mechanisms, shallow and deep moist convection and the pyro-convection (dry or moist) induced by vegetation fires, on the distribution of biomass burning CO emissions in the troposphere.

Abstract. We describe an estimation technique for biomass burning emissions in South America based on a combination of remote-sensing fire products and field observations, the Brazilian Biomass Burning Emission Model (3BEM). For each fire pixel detected by remote sensing, the mass of the emitted tracer is calculated based on field observations of fire properties related to the type of vegetation burning. The burnt area is estimated from the instantaneous fire size retrieved by remote sensing, when available, or from statistical properties of the burn scars. The sources are then spatially and temporally distributed and assimilated daily by the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) in order to perform the prognosis of related tracer concentrations. Three other biomass burning inventories, including GFEDv2 and EDGAR, are simultaneously used to compare the emission strength in terms of the resultant tracer distribution. We also assess the effect of using the daily time resolution of fire emissions by including runs with monthly-averaged emissions. We evaluate the performance of the model using the different emission estimation techniques by comparing the model results with direct measurements of carbon monoxide both near-surface and airborne, as well as remote sensing derived products. The model results obtained using the 3BEM methodology of estimation introduced in this paper show relatively good agreement with the direct measurements and MOPITT data product, suggesting the reliability of the model at local to regional scales.

Geostationary Operational Environmental Satellite (GOES)-14 imager was operated by National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode that emulates the high-temporal resolution sampling of the Advanced Baseline Imager (ABI) on the next generation GOES-R series. Imagery with a refresh rate of 1 min of many phenomena were acquired, including clouds, convection, fires, smoke, and hurricanes, including 6 days of Hurricane Sandy through landfall. NOAA had never before operated a GOES in a nearly continuous 1-min mode for such an extended period of time, thereby making these unique datasets to explore the future capabilities possible with GOES-R. The next generation GOES-R imager will be able to routinely take mesoscale (1000 km×1000 km) images every 30 s (or two separate locations every minute). These images can be acquired even while scanning continental United States and full disk images. These high time-resolution images from the GOES-14 imager are being used to prepare for the GOES-R era and its advanced imager. This includes both the imagery and quantitative derived products such as cloud-top cooling. Several animations are included to showcase the rapid change of the many phenomena observed during super rapid scan operations for GOES-R (SRSOR).

A linear form of the radiative transfer equation (RTE) is formulated for the direct and simultaneous estimation of temperature and absorbing constituent profiles (e.g., water vapor, ozone, methane) from observations of spectral radiances. This unique linear form of the RTE results from a definition for the deviation of the true gas concentration profiles from an initial specification in terms of the deviation of their effective temperature profiles from the true atmospheric temperature profile. The effective temperature profile for any absorbing constituent is that temperature profile which satisfies the observed radiance spectra under the assumption that the initial absorber concentration profile is correct. Differences between the effective temperature, derived for each absorbing constituent, and the true atmospheric temperature are proportional to the error of the initial specification of the gas concentration profiles. The gas concentration profiles are thus specified after inversion of the linearized RTE from the retrieved effective temperature profiles assuming that one of the assumed concentration profiles is known (e.g., CO(2)). Because the solution is linear and simultaneous, the solution is computationally efficient. This efficiency is important for dealing with radiance spectra containing several thousand radiance observations as obtained from current airborne and planned future spaceborne interferometer spectrometer sounders. Here the solution is applied to spectral radiance observations simulated for current filter radiometers and planned spectrometers to demonstrate the anticipated improvement in future satellite sounding performance as a result of improved instrumentation and associated sounding retrieval methodology.

The Advanced Baseline Imager (ABI) is a passive imaging radiometer on-board National Oceanic and Atmospheric Administration’s (NOAA) Geostationary Operational Environmental Satellites-R (GOES-R) series. Its bands 7 to 16 are categorized as infrared (IR) bands, sampling within a spectral range of 3.9 to 13.3 μm in mid-wave infrared (MWIR) and long-wave infrared (LWIR) regions. ABI provides variable area imagery and radiometric information of Earth’s surface, atmosphere, and cloud cover. All of the IR bands are calibrated on-orbit in reference to an internal blackbody. While the ABI aboard the GOES-16 satellite has been working properly, an anomaly with GOES-17 ABI’s cooling system, specifically its loop heat pipe (LHP) subsystem, prevents heat from being efficiently transferred from the ABI electronics to the radiator to be dissipated into space. As a consequence, the heat accumulates inside the instrument, so the temperatures of its key components for IR calibration, including the focal plane modules (FPMs), scan mirrors, and blackbody, cannot be maintained at their designed operational levels. As an example, the temperatures of MWIR and LWIR FPMs, where IR detectors are located, are currently operated at a baseline temperature of ∼20 K warmer than the design and vary by as many as 27 K diurnally. This causes severe degradation to the data quality of ABI IR Level 1b radiance and subsequent Level 2+ products during the hot period of the day. Significant progress has been made to mitigate the effects of the LHP anomaly to optimize the IR performance of GOES-17 ABI. We summarize the efforts made by NOAA’s GOES-R Calibration Working Group, working collaboratively with other teams, to evaluate and alleviate the negative impacts of warmer and floating FPM temperatures on ABI IR calibration, and assess the IR performance accordingly.

Hyperspectral sounder data is used for retrieval of atmospheric temperature, moisture and trace gas profiles, surface temperature and emissivity, and cloud and aerosol optical properties. This large volume of data is 3-D in nature with many scan lines containing cross-track footprints, each with thousands of IR channels. Unlike hyperspectral imager data compression, hyperspectral sounder data compression is desired to be lossless or near-lossless to avoid substantial degradation of the geophysical retrieval. For this new class of data for compression studies, a lossless compression algorithm combining the context-based adaptive lossless image codec (CALIC) and a novel bias-adjusted reordering (BAR) scheme is presented. The 3-D data are arranged into two dimensions with the original 2-D spatial domain converted into one dimension using a continuous scan order. In the BAR scheme, the data are reordered such that the bias-adjusted distance between any two neighboring vectors is minimized. The result is then encoded using the CALIC algorithm with significant compression gains over using the CALIC algorithm alone.

A nonlinear sounding retrieval algorithm is used to produce vertical-temperature and water-vapor profiles from coincident observations taken by the airborne High-resolution Interferometer Sounder (HIS) and the ground-based Atmospheric Emitted Radiance Interferometer (AERI) during the SUbsonic Contrails and Clouds Effects Special Study (SUCCESS). Also, clear sky Geostationary Operational Environmental Satellite (GOES) and AERI radiance measurements, achieved on a daily real-time basis at the Department of Energy's Oklahoma CART (Cloud and Radiation Testbed) site, are used to demonstrate the current profiling capability by use of simultaneous geostationary satellite and ground-based remote sensing observations under clear-sky conditions. The discrepancy principle, a method to find the proper smoothing parameters from the minimum value between the normalized spectral residual norm and the a priori upper bound, is used to demonstrate the feasibility and effectiveness of on-line simultaneous tuning of the multiple weighting and smoothing parameters from the combined satellite/airborne and ground-based measurements for the temperature and water-vapor retrieval in this nonlinear-retrieval process. An objective method to determine the degrees of freedom (d.f.) of the observation signal is derived. The d.f. of the radiance signal for the combined GOES and AERI measurements is larger than that for either instrument alone; while the d.f. of the observation signal for the combined GOES and AERI measurements is larger than that for either instrument alone and of the combined GOES and AERI measurements. The use of simultaneous clear-sky AERI and GOES data now provides improved vertical temperature and moisture soundings on an hourly basis for use in the Atmospheric Radiation Measurement program [J. Appl. Meteorol. 37, 875 (1998)].

Regional assimilation experiments of clear-sky Atmospheric Infrared Sounder (AIRS) radiances were performed using the gridpoint statistical interpolation three-dimensional variational assimilation system coupled to the weather research and forecasting model. The data assimilation system and forecast model used in this study are separate community models; it cannot be assumed that the coupled systems work optimally. Tuning was performed on the data assimilation system and forecast model. Components tuned included the background error covariance matrix, the satellite radiance bias correction, the quality control procedures for AIRS radiances, the forecast model resolution, and the infrared channel selection. Assimilation metrics and diagnostics from the assimilation system were used to identify problems when combining separate systems. Forecasts initiated from analyses after assimilation were verified with model analyses, rawinsondes, nonassimilated satellite radiances, and 24 h–accumulated precipitation. Assimilation of clear sky AIRS radiances showed the largest improvement in temperature and radiance brightness temperature bias when compared with rawinsondes and satellite observations, respectively. Precipitation skill scores displayed minor changes with AIRS radiance assimilation. The 00 and 12 coordinated universal time (UTC) forecasts were typically of better quality than the 06 and 18 UTC forecasts, possibly due to the amount of AIRS data available for each assimilation cycle.

Satellite retrievals of column mass loading of volcanic ash are incorporated into the HYSPLIT transport and dispersion modeling system for source determination, bias correction, and forecast verification of probabilistic ash forecasts of a short eruption of Bezymianny in Kamchatka. The probabilistic forecasts are generated with a dispersion model ensemble created by driving HYSPLIT with 31 members of the NOAA global ensemble forecast system (GEFS). An inversion algorithm is used for source determination. A bias correction procedure called cumulative distribution function (CDF) matching is used to very effectively reduce bias. Evaluation is performed with rank histograms, reliability diagrams, fractions skill score, and precision recall curves. Particular attention is paid to forecasting the end of life of the ash cloud when only small areas are still detectable in satellite imagery. We find indications that the simulated dispersion of the ash cloud does not represent the observed dispersion well, resulting in difficulty simulating the observed evolution of the ash cloud area. This can be ameliorated with the bias correction procedure. Individual model runs struggle to capture the exact placement and shape of the small areas of ash left near the end of the clouds lifetime. The ensemble tends to be overconfident but does capture the range of possibilities of ash cloud placement. Probabilistic forecasts such as ensemble-relative frequency of exceedance and agreement in percentile levels are suited to strategies in which areas with certain concentrations or column mass loadings of ash need to be avoided with a chosen amount of confidence.

ABSTRACT: Despite decades of theoretical research and observational studies, a good understanding of tropical cyclone genesis (TCG) remains elusive. One school of theories proposes that TCG within an African Easterly Wave results from “bottom-up” development of cyclonic vorticity that is contingent upon favorable conditions in the lower-troposphere and boundary layer. Our observational study suggests that while lower-tropospheric forcing is a necessary condition for this type of TCG, it may not be sufficient in some cases, and that environmental conditions in the upper levels can have an influence. Specifically, we find evidence to suggest that pre-TCG upper-tropospheric flow patterns characterized by core-connecting outflow vents to the environment can in certain situations provide a modulating effect on Atlantic tropical disturbances trying to develop. Patterns of near-environment upper-level inertial stability, divergence, outflow setup, and mass evacuation are identified and related to surface development. The study employs high-resolution satellite-derived wind data, aircraft GPS dropwindsondes, composite fields, multivariate objective analyses, and case studies to help identify conditions in the upper-level environment that can play a role in Atlantic TCG events. Keywords: tropical cyclone, genesis, satellite, observations

Flash droughts are characterized by a period of unusually rapid drought intensification over sub-seasonal time scales that often take vulnerable stakeholders by surprise given their rapid onset. Various studies have shown that flash drought is more likely to develop when extreme weather conditions persist over the same region for several weeks or longer. Though precipitation deficits over some period of time are a prerequisite for drought, their presence alone is unlikely to lead to flash drought because a lack of precipitation is only one of several factors that contribute to rapid drought development. When below normal precipitation occurs alongside other extreme weather anomalies such as intense heat that enhance atmospheric evaporative demand, their co-occurrence can lead to a rapid depletion of root zone soil moisture content due to increased evapotranspiration. This in turn can lead to a rapid increase in vegetation moisture stress and the onset of flash drought conditions. Several recent studies have used quantitative definitions based on rapid changes in a given drought monitoring dataset to identify flash droughts in the climatological record. Here, we build upon these recent studies by developing a new flash drought intensity index that accounts not only for their rapid rate of intensification, but also for how severe the drought conditions become during and after the period of rapid intensification. The method includes two components that together capture the suddenness of flash drought development (faster intensification corresponds to a more severe flash drought) and the actual drought severity after the rapid intensification period ends (severe drought conditions lasting for a longer period correspond to a more severe flash drought). The motivation behind this method is the desire to account for both the “flash” and “drought” aspects of flash drought because both of these characteristics influence how people view flash droughts. Thus, a metric that considers both of these aspects provides a more comprehensive assessment of flash drought intensity and its impacts on the environment. In this talk, we will present the proposed flash drought intensity index methodology, along with results from individual case studies and a 40-year climatology to illustrate its use.

Background error covariance (B) matrix is critical for variational data assimilation as it greatly affects the analyses of three-dimensional variational assimilation. The National Meteorological Center method was used to estimate the B matrix using the forecasts from the Advanced Research Weather Research and Forecasting regional model. To further understand and evaluate the newly generated regional B matrix, its characteristics were compared with the global B estimated from the Global Forecast System model. Sensitivity experiments were undertaken by changing the horizontal length-scales and standard deviations of the B matrix, and its impacts on the typhoon forecast were also examined. Verification against radiosonde observations showed that the varying horizontal length-scale has a significant positive impact on the 24-h forecast of temperature, specific humidity, u-wind, and v-wind. On the other hand, changing standard deviations of the B matrix has a slight influence only on the specific humidity and wind (u-component) forecast. Compared with the global B, the tuned regional B showed improvements in temperature forecasts. In addition, using the tuned regional B also led to a positive impact on the typhoon (Saola, Damrey, and Haikui) track forecasts as compared with the untuned B and global B.

The advanced baseline imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-R Series is a great improvement compared to the legacy GOES imager. For example, there are more spectral bands at improved spatial resolution and more frequent imagery. The vast majority of the images generated by the ABIs are free of visual defects, well calibrated, and produced in a timely fashion. Yet, there are rare times when visual artifacts, or anomalies, occur. Our study highlights and explains a number of these artifacts, some of which are traditional imagery defects for imagers such as striping and stray light, and colorfully named artifacts such as “caterpillar tracks” and “shark fins.” In addition, multiple resources are presented for more information about image quality and near-real-time image monitoring.

Hyperspectral infrared remote sensing can provide the information about temperature and humidity of the atmosphere at high vertical resolution and high accuracy. To assimilate its radiances directly, we must correct biases between the observed radiances and the simulated ones from the model first guess, caused by systematic error of radiances and by the radiative transfer model and assimilating system. The method used for bias correction was developed for global models, and its adaptation to regional models raises further questions. This study is based on coupling the mesoscale numerical model weather research and forecast and gridpoint statistical interpolation assimilation system, using adaptive variational bias correction (VarBC) to the scan angle and air-mass factor, and investigates the characteristics of bias correction coefficients for the regional model. It was found that advanced infrared sounder (AIRS) channels located in the 15-μm CO2 absorption band had large scan bias, and that its nadir bias had a time dependence, which is probably due to the bias from the radiative transfer model. By contrast, other channels had small scan bias and weak time dependence. In air-mass bias correction, predictors of zenith and temperature lapse rate had huge oscillations due to variations in data coverage from the regional models. The effect of this scheme on correction in a regional model was verified via the histogram analysis of innovation. The verification showed that correction on most of the channels got satisfactory results except for several land surface channels. The corrected histogram satisfied the requirement of an unbiased normal distribution. In a typhoon forecast experiment, the influence of radiance bias correction on forecast result was tested. It showed that, compared with parameters from the global model, regional radiance correction parameters study improved the prediction of the typhoon 72-h forecast.

This paper conducts a preliminary assessment of the cloud detection capability of the Japanese Global Imager (GLI). Cloud detection results from the satellite borne instrument are compared to other satellite, aircraft and ground-based observations. The performance is similar to that of the MODIS results.

Abstract The next‐generation geostationary satellites are expected to have hyperspectral infrared (IR) sounders, providing hemispheric coverage of satellite‐derived vertical profiles of temperature, moisture, and wind in clear skies and above clouds. Derivation of winds, or atmospheric motion vectors (AMVs), from IR hyperspectral sounders was first demonstrated using Aqua Atmospheric Infrared Sounder retrievals. The AMVs on discrete pressure levels (3D winds) provided, for the first time, vertical profiles of wind information in the polar regions. Since then, the capability has been extended to tracking features in global profile retrievals of humidity and ozone derived from Cross‐track Infrared Sounder (CrIS) and Infrared Atmospheric Sounding Interferometer radiances. And, it is now demonstrated for the first time globally using retrievals at single field‐of‐view resolution from successive overpasses of three CrIS instruments on NOAA‐21, NOAA‐20, and SNPP flying in formation. 3D winds from polar‐orbiting satellites can provide all‐latitude (“global”) coverage giving insight into capabilities when all geostationary satellites are equipped with IR sounders.

The instantaneous daytime geographical distribution and radiative effects of high thin clouds (optical thickness < 5) are investigated on the basis of the CloudSat Cloud Profiling Radar (CPR) radiative flux and cloud classification products. The regional features of the fraction and radiative effects of high thin clouds are associated with ITCZ, SPCZ and mid-latitude storm track regions. High thin clouds have positive net cloud-induced radiative effect (CRE) at the top of the atmosphere (TOA) and negative net CRE at the bottom of the atmosphere (BOA). The magnitudes of TOA and BOA CREs depend on cloud optical thickness, cloud fraction and geographical location. The magnitude of the net CRE of high thin clouds increases at both TOA and BOA as cloud optical thickness increases. Net CRE at both TOA and BOA contributes to a positive net CRE in-atmosphere and warms the atmosphere regardless of cloud fraction. The global annual mean of the net CRE multiplied by cloud fraction is 0.49 W/m2 at TOA, -0.54 W/m2 at BOA and 1.03 W/m2 in-atmosphere. The most radiatively effective cloud optical thickness of a high thin cloud is between 1-2 for the TOA and in-atmosphere CREs or 3-4 for the BOA CRE.

Abstract The January 2022 eruption of the Hunga Tonga–Hunga Ha'apai volcano in the South Pacific emitted significant sulfur dioxide into the atmosphere, forming a large stratospheric cloud. This study employs the HYSPLIT model, a Lagrangian atmospheric transport and dispersion model, along with satellite retrievals of cloud properties to model the long range transport of the cloud. To reduce the uncertainty and complexity of modeling the near‐source behavior of the umbrella cloud, we utilize a data insertion technique that initializes the model at a downwind plume location. Satellite retrievals provide estimates of column mass loading and plume top height, though the plume top height may be uncertain above the tropopause. Additionally, the vertical mass distribution must be estimated by making assumptions about the cloud thickness. We use a back trajectory analysis to provide better estimations of plume top height and thickness. Our findings reveal that trajectory‐derived cloud top heights substantially exceeded satellite estimates, with 60% ranging between 20 and 40 km, compared to most satellite‐derived estimates being around 15 km. Long range 5‐day forecasts produced with data insertion using the revised cloud top heights and estimated thickness are compared with forecasts using retrieved cloud top heights and an assumed simple thickness of 1 km, and a control run initiated from the vent at the eruption start time. A qualitative comparison with satellite retrievals and data from ground based lidar stationed at Réunion Island shows the use of the back trajectory analysis significantly improves the forecast.

Channel-to-channel co-registration is an important performance metric for the Geostationary Operational Environmental Satellite (GOES) Imager, and large co-registration errors can have a significant impact on the reliability of derived products that rely on combinations of multiple infrared (IR) channels. Affected products include the cloud mask, fog and fire detection. This is especially the case for GOES-13, in which the co-registration error between channels 2 (3.9 μm) and 4 (10.7 μm) can be as large as 1 pixel (or ∼4 km) in the east-west direction. The GOES Imager IR channel-to-channel co-registration characterization (GII4C) algorithm is presented, which allows a systematic calculation of the co-registration error between GOES IR channel image pairs. The procedure for determining the co-registration error as a function of time is presented. The algorithm characterizes the co-registration error between corresponding images from two channels by spatially transforming one image using the fast Fourier transformation resampling algorithm and determining the distance of the transformation that yields the maximum correlation in brightness temperature. The GII4C algorithm is an area-based approach which does not depend on a fixed set of control points that may be impacted by the presence of clouds. In fact, clouds are a feature that enhances the correlations. The results presented show very large correlations over the majority of Earth-viewing pixels, with stable algorithm results. Verification of the algorithm output is discussed, and a global spatial-spectral gradient asymmetry parameter is defined. The results show that the spatial-spectral gradient asymmetry is strongly correlated to the co-registration error and can be an effective global metric for the quality of the channel-to-channel co-registration characterization algorithm. Implementation of the algorithm in the GOES ground system is presented. This includes an offline component to determine the time dependence of the co-registration errors and a real-time component to correct the co-registration errors based on the inputs from the offline component.

The Atmospheric Emitted Radiance Interferometer (AERI) is a Ground-based High Resolution Interferometer Sounder (GB-HIS) used to produce temperature and water vapor profiles every ten minutes in the Planetary Boundary Layer (PBL), the lowest 2.5 km of the earth's atmosphere. AERI measures infrared (IR) radiation (3 to 18 µm) passively, yielding high spectral resolution IR radiance (Δv= 0.5 cm-1). Meteorological information is contained within the radiance spectra as shown in Figure 1. These radiance spectra are transformed to vertical temperature and water vapor profiles by inverting the IR RadiativeTransfer Equation (RTE) (Smith, 1994). High temperature and water vapor retrieval skill, within the PBL, have been shown in several field experiments using AERI (Feltz, 1994).