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Qing Yue, Brian H. Kahn, Eric J. Fetzer, Mathias Schreier, Sun Wong, Xiuhong Chen, and Xianglei Huang

Abstract

The authors present a new method to derive both the broadband and spectral longwave observation-based cloud radiative kernels (CRKs) using cloud radiative forcing (CRF) and cloud fraction (CF) for different cloud types using multisensor A-Train observations and MERRA data collocated on the pixel scale. Both observation-based CRKs and model-based CRKs derived from the Fu–Liou radiative transfer model are shown. Good agreement between observation- and model-derived CRKs is found for optically thick clouds. For optically thin clouds, the observation-based CRKs show a larger radiative sensitivity at TOA to cloud-cover change than model-derived CRKs. Four types of possible uncertainties in the observed CRKs are investigated: 1) uncertainties in Moderate Resolution Imaging Spectroradiometer cloud properties, 2) the contributions of clear-sky changes to the CRF, 3) the assumptions regarding clear-sky thresholds in the observations, and 4) the assumption of a single-layer cloud. The observation-based CRKs show the TOA radiative sensitivity of cloud types to unit cloud fraction change as observed by the A-Train. Therefore, a combination of observation-based CRKs with cloud changes observed by these instruments over time will provide an estimate of the short-term cloud feedback by maintaining consistency between CRKs and cloud responses to climate variability.

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Meinhard Seefeldner, Andreas Oppenrieder, Dieter Rabus, Joachim Reuder, Mathias Schreier, Peter Hoeppe, and Peter Koepke

Abstract

A versatile two-axis tracking system with datalogger is presented. It is designed with regard to high pointing accuracy, high torque and mechanical load, high accuracy of the data acquisition, extended weather resistance, remote operability, and considerable freedom from maintenance on site. The system can be used for a variety of pointing devices and also for completely different positioning tasks, such as, for example, the operation of samplers. Depending on the version of the gear box, the maximum absolute pointing error of the tracking system is 19–71 arc min, the angular backlash of its axes is 2–35 arc min, and its peak torque is 15–20 Nm. The maximum mechanical load on the power takeoff shaft is 550 N. The mechanical, electronic, and software design is considerably modular. The modules can be used in various combinations or even stand alone, and they can be modified for future applications without modifying the whole system. The electronics is based on a programmable logic controller, which can in addition to the tracking system and datalogger also serve any pointing and further measurement devices. This facilitates an easy setup of a wide range of different measuring stations. They all have the advantage of being based on the single time process of the programmable logic controller. Four examples of the system have shown very satisfying results in up to 4-yr-long 24-h operation from alpine to seaside environments.

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Paul W. Staten, Brian H. Kahn, Mathias M. Schreier, and Andrew K. Heidinger

Abstract

This paper describes a cloud type radiance record derived from NOAA polar-orbiting weather satellites using cloud properties retrieved from the Advanced Very High Resolution Radiometer (AVHRR) and spectral brightness temperatures (T b) observed by the High Resolution Infrared Radiation Sounder (HIRS). The authors seek to produce a seamless, global-scale, long-term record of cloud type and T b statistics intended to better characterize clouds from seasonal to decadal time scales. Herein, the methodology is described in which the cloud type statistics retrieved from AVHRR are interpolated onto each HIRS footprint using two cloud classification methods. This approach is tested over the northeast tropical and subtropical Pacific Ocean region, which contains a wide variety of cloud types during a significant ENSO variation from 2008 to 2009. It is shown that the T b histograms sorted by cloud type are realistic for all HIRS channels. The magnitude of T b biases among spatially coincident satellite intersections over the northeast Pacific is a function of cloud type and wavelength. While the sign of the bias can change, the magnitudes are generally small for NOAA-18 and NOAA-19, and NOAA-19 and MetOp-A intersections. The authors further show that the differences between calculated standard deviations of cloud-typed T b well exceed intersatellite calibration uncertainties. The authors argue that consideration of higher-order statistical moments determined from spectral infrared observations may serve as a useful long-term measure of small-scale spatial changes, in particular cloud types over the HIRS–AVHRR observing record.

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Qing Yue, Brian H. Kahn, Eric J. Fetzer, Sun Wong, Xianglei Huang, and Mathias Schreier

Abstract

Observations from multiple sensors on the NASA Aqua satellite are used to estimate the temporal and spatial variability of short-term cloud responses (CR) and cloud feedbacks λ for different cloud types, with respect to the interannual variability within the A-Train era (July 2002–June 2017). Short-term cloud feedbacks by cloud type are investigated both globally and locally by three different definitions in the literature: 1) the global-mean cloud feedback parameter λ GG from regressing the global-mean cloud-induced TOA radiation anomaly ΔR G with the global-mean surface temperature change ΔT GS; 2) the local feedback parameter λ LL from regressing the local ΔR with the local surface temperature change ΔT S; and 3) the local feedback parameter λ GL from regressing global ΔR G with local ΔT S. Observations show significant temporal variability in the magnitudes and spatial patterns in λ GG and λ GL, whereas λ LL remains essentially time invariant for different cloud types. The global-mean net λ GG exhibits a gradual transition from negative to positive in the A-Train era due to a less negative λ GG from low clouds and an increased positive λ GG from high clouds over the warm pool region associated with the 2015/16 strong El Niño event. Strong temporal variability in λ GL is intrinsically linked to its dependence on global ΔR G, and the scaling of λ GL with surface temperature change patterns to obtain global feedback λ GG does not hold. Despite the shortness of the A-Train record, statistically robust signals can be obtained for different cloud types and regions of interest.

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Derek J. Posselt, Longtao Wu, Mathias Schreier, Jacola Roman, Masashi Minamide, and Bjorn Lambrigtsen

Abstract

Forecast observing system simulation experiments (OSSEs) are conducted to assess the potential impact of geostationary microwave (GeoMW) sounder observations on numerical weather prediction forecasts. A regional OSSE is conducted using a tropical cyclone (TC) case that is very similar to Hurricane Harvey (2017), as hurricanes are among the most devastating of weather-related natural disasters, and hurricane intensity continues to pose a significant challenge for numerical weather prediction. A global OSSE is conducted to assess the potential impact of a single GeoMW sounder centered over the continental United States versus two sounders positioned at the current locations of the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES) East and West. It is found that assimilation of GeoMW soundings result in better characterization of the TC environment, especially before and during intensification, which leads to significant improvements in forecasts of TC track and intensity. TC vertical structure (warm core thermal perturbation and horizontal wind distribution) is also substantially improved, as are the surface wind and precipitation extremes. In the global OSSE, assimilation of GeoMW soundings leads to slight improvement globally and significant improvement regionally, with regional impact equal to or greater than nearly all other observation types.

Significance Statement

This work seeks to determine the impact of a new geostationary microwave (GeoMW) sounder on tropical cyclone forecasts in particular, and on weather forecasts in general. It does so by assimilating simulated GeoMW sounder data into two different forecast models: one global and one regional. The data have a small positive impact globally, and a significant positive impact over the region viewed by the GeoMW instrument. In particular, assimilation of GeoMW data has a significant and positive impact on forecasts of tropical cyclone track, strength, and structure.

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Ali Behrangi, Bin Guan, Paul J. Neiman, Mathias Schreier, and Bjorn Lambrigtsen

Abstract

Atmospheric rivers (ARs) are often associated with extreme precipitation, which can lead to flooding or alleviate droughts. A decade (2003–12) of landfalling ARs impacting the North American west coast (between 32.5° and 52.5°N) is collected to assess the skill of five commonly used satellite-based precipitation products [T3B42, T3B42 real-time (T3B42RT), CPC morphing technique (CMORPH), PERSIANN, and PERSIANN–Cloud Classification System (CCS)] in capturing ARs’ precipitation rate and pattern. AR detection was carried out using a database containing twice-daily satellite-based integrated water vapor composite observations. It was found that satellite products are more consistent over ocean than land and often significantly underestimate precipitation rate over land compared to ground observations. Incorrect detection of precipitation from IR-based methods is prevalent over snow and ice surfaces where microwave estimates often show underestimation or missing data. Bias adjustment using ground observation is found very effective to improve satellite products, but it also raises concern regarding near-real-time applicability of satellite products for ARs. The analysis using individual case studies (6–8 January and 13–14 October 2009) and an ensemble of AR events suggests that further advancement in capturing orographic precipitation and precipitation over cold and frozen surfaces is needed to more reliably quantify AR precipitation from space.

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Shaima L. Nasiri, H. Van T. Dang, Brian H. Kahn, Eric J. Fetzer, Evan M. Manning, Mathias M. Schreier, and Richard A. Frey

Abstract

Comparisons are described for infrared-derived cloud products retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) using measured spatial response functions obtained from prelaunch AIRS calibration. One full day (1 January 2005) of global collection-5 MODIS and version-5 AIRS retrievals of cloud-top temperature Tc, effective cloud fraction f, and derived effective brightness temperature Tb ,e is investigated. Comparisons of Tb ,e demonstrate that MODIS and AIRS are essentially radiatively consistent and that MODIS Tb ,e is 0.62 K higher than AIRS Tb ,e for all scenes, increasing to 1.43 K for cloud described by AIRS as single layer and decreasing to 0.50 K for two-layer clouds. Somewhat larger differences in Tc and f are observed between the two instruments. The magnitudes of differences depend partly on whether MODIS uses a CO2-slicing or 11-μm brightness temperature window retrieval method. Some cloud- and regime-type differences and similarities between AIRS and MODIS cloud products are traceable to the assumptions made about the number of cloud layers in AIRS and also to the MODIS retrieval method. This (partially) holistic comparison approach should be useful for ongoing algorithm refinements, rigorous assessments of climate applicability, and establishment of the capability of synergistic MODIS and AIRS retrievals for improved cloud quantities and also should be useful for future observations to be made by the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP).

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