scholarly journals Assessing NOAA-16 HIRS Radiance Accuracy Using Simultaneous Nadir Overpass Observations from AIRS

2007 ◽  
Vol 24 (9) ◽  
pp. 1546-1561 ◽  
Author(s):  
Likun Wang ◽  
Changyong Cao ◽  
Pubu Ciren
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
High Spectral Resolution ◽  
Temperature Dependent ◽  
Atmospheric Infrared Sounder ◽  
Intersatellite Calibration ◽  
Calibration Accuracy ◽  
Calibration Techniques ◽  
Accuracy Assessments ◽  
Climate Studies

Abstract The High-Resolution Infrared Radiation Sounder (HIRS) has been carried on NOAA satellites for more than two decades, and the HIRS data have been widely used for geophysical retrievals, climate studies, and radiance assimilation for numerical weather prediction models. However, given the legacy of the filter-wheel radiometer originally designed in the 1970s, the HIRS measurement accuracy is neither well documented nor well understood, despite the importance of this information for data users, instrument manufacturers, and calibration scientists. The advent of hyperspectral sounders, such as the Atmospheric Infrared Sounder (AIRS), and intersatellite calibration techniques makes it possible to independently assess the accuracy of the HIRS radiances. This study independently assesses the data quality and calibration accuracy of HIRS by comparing the radiances between HIRS on NOAA-16 and AIRS on Aqua with simultaneous nadir overpass (SNO) observations for the year 2004. The results suggest that the HIRS radiometric bias relative to the AIRS-convolved HIRS radiance is on the order of ∼0.5 K, except channel 16, which has a bias of 0.8 K. For all eight spectrally overlapped channels, the observations by HIRS are warmer than the corresponding AIRS-convolved HIRS channel. Other than channel 16, the biases are temperature dependent. The root causes of the bias can be traced to a combination of the HIRS blackbody emissivity, nonlinearity, and spectral uncertainties. This study further demonstrates the utility of high-spectral-resolution radiance measurements for high-accuracy assessments of broadband radiometer calibration with the SNO observations.

scholarly journals Improved observations of turbulence dissipation rates from wind profiling radars

2017 ◽  
Vol 10 (7) ◽  
pp. 2595-2611 ◽  
Author(s):  
Katherine McCaffrey ◽  
Laura Bianco ◽  
James M. Wilczak
Keyword(s):  
Boundary Layer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Spectral Width ◽  
High Spectral Resolution ◽  
Dissipation Rates ◽  
Sonic Anemometers ◽  
Processing Algorithms ◽  
Processing Techniques ◽  
Numerical Weather Prediction Models

Abstract. Observations of turbulence dissipation rates in the planetary boundary layer are crucial for validation of parameterizations in numerical weather prediction models. However, because dissipation rates are difficult to obtain, they are infrequently measured through the depth of the boundary layer. For this reason, demonstrating the ability of commonly used wind profiling radars (WPRs) to estimate this quantity would be greatly beneficial. During the XPIA field campaign at the Boulder Atmospheric Observatory, two WPRs operated in an optimized configuration, using high spectral resolution for increased accuracy of Doppler spectral width, specifically chosen to estimate turbulence from a vertically pointing beam. Multiple post-processing techniques, including different numbers of spectral averages and peak processing algorithms for calculating spectral moments, were evaluated to determine the most accurate procedures for estimating turbulence dissipation rates using the information contained in the Doppler spectral width, using sonic anemometers mounted on a 300 m tower for validation. The optimal settings were determined, producing a low bias, which was later corrected. Resulting estimations of turbulence dissipation rates correlated well (R2 = 0. 54 and 0. 41) with the sonic anemometers, and profiles up to 2 km from the 449 MHz WPR and 1 km from the 915 MHz WPR were observed.

Intercalibration of Broadband Geostationary Imagers Using AIRS

2009 ◽  
Vol 26 (4) ◽  
pp. 746-758 ◽  
Author(s):  
Mathew M. Gunshor ◽  
Timothy J. Schmit ◽  
W. Paul Menzel ◽  
David C. Tobin
Keyword(s):  
Spectral Resolution ◽  
Spectral Response ◽  
High Spectral Resolution ◽  
Spectral Gaps ◽  
Atmospheric Infrared Sounder ◽  
Earth Observing System ◽  
First Approximation ◽  
Atmospheric Sounding ◽  
Calibration Accuracy ◽  
Polar Orbiting Satellite

Abstract Geostationary simultaneous nadir observations (GSNOs) are collected for Earth Observing System (EOS) Atmospheric Infrared Sounder (AIRS) on board Aqua and a global array of geostationary imagers. The imagers compared in this study are on (Geostationary Operational Environmental Satellites) GOES-10, GOES-11, GOES-12, (Meteorological Satellites) Meteosat-8, Meteosat-9, Multifunctional Transport Satellite-IR (MTSAT-IR), and Fenguyun-2C (FY-2C). It has been shown that a single polar-orbiting satellite can be used to intercalibrate any number of geostationary imagers. Using a high-spectral-resolution infrared sensor, in this case AIRS, brings this method closer to an absolute reckoning of imager calibration accuracy based on laboratory measurements of the instrument’s spectral response. An intercalibration method is presented here, including a method of compensating for AIRS’ spectral gaps, along with results for approximately 22 months of comparisons. The method appears to work very well for most bands, but there are still unresolved issues with bands that are not spectrally covered well by AIRS (such as the water vapor bands and the 8.7-μm band on Meteosat). To the first approximation, most of the bands on the world’s geostationary imagers are reasonably well calibrated—that is, they compare to within 1 K of a standard reference (AIRS). The next step in the evolution of geostationary intercalibration is to use Infrared Atmospheric Sounding Interferometer (IASI) data. IASI is a high-spectral-resolution instrument similar to AIRS but without significant spectral gaps.

scholarly journals Improved Observations of Turbulence Dissipation Rates from Wind Profiling Radars

2016 ◽  
Author(s):  
Katherine McCaffrey ◽  
Laura Bianco ◽  
James M. Wilczak
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Spectral Width ◽  
High Spectral Resolution ◽  
Spectral Moments ◽  
Dissipation Rates ◽  
Sonic Anemometers ◽  
Processing Algorithms ◽  
Processing Techniques ◽  
Numerical Weather Prediction Models

Abstract. Observations of turbulence in the planetary boundary layer are crucial for validation of parameterizations in numerical weather prediction models. However, these observations are sparse. For this reason, demonstrating the ability of commonly-used wind profiling radars (WPRs) to measure turbulence dissipation rates would be greatly beneficial. During the XPIA field campaign at the Boulder Atmospheric Observatory, two WPRs operated in an optimized configuration, using high spectral resolution for increased accuracy of Doppler spectral width, specifically chosen to measure turbulence from a vertically-pointing beam only. Multiple post-processing techniques, including different numbers of spectral averages and peak-processing algorithms for calculating spectral moments, were analyzed to determine the most accurate procedures for measuring turbulence dissipation rates using the information contained in the Doppler spectral width, and compared to sonic anemometers mounted on a 300-meter tower. The optimal settings were determined, producing a constant low bias, which was later corrected. Resulting measurements of turbulence dissipation rates correlated well (R2 = 0.57) with sonic anemometers, and profiles up to 2 km from the 449-MHz WPR and 1 km from the 915-MHz WPR were observed.

scholarly journals AtmoSwing: Analog Technique Model for Statistical Weather forecastING and downscalING

2019 ◽  
Author(s):  
Pascal Horton
Keyword(s):  
Climate Change ◽  
Prediction Models ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Swiss Alps ◽  
Raspberry Pi ◽  
Statistical Relationship ◽  
Impact Studies ◽  
Climate Studies

Abstract. Analog methods (AMs) allow for the prediction of local meteorological variables of interest (predictand) such as the daily precipitation, on the basis of synoptic variables (predictors). They can rely on outputs of numerical weather prediction models in the context of operational forecasting or outputs of climate models in the context of climate impact studies. AMs require low computing capacity and have demonstrated a useful potential for application in several contexts. AtmoSwing is an open source software written in C++ that implements AMs in a flexible manner so that different variants can be handled dynamically. It comprises four tools: a Forecaster that performs operational forecasts, a Viewer for displaying the results, a Downscaler for climate studies, and an Optimizer for inferring the relationship between the predictand and predictors. The Forecaster handles every required processing internally, such as operational predictor downloading (when possible) and reading, grid interpolation, etc., without external scripts or file conversion. The processing of a forecast is extremely low-intensive in terms of computing infrastructure and can even run on a Raspberry Pi computer. It provides valuable results, as revealed by a three-year-long operational forecast in the Swiss Alps. The Viewer displays the forecasts in an interactive GIS environment. It contains several layers of syntheses and details in order to provide a quick overview of the potential critical events in the upcoming days, as well as the possibility for the user to delve into the details of the forecasted predictand and criteria distributions. The Downscaler allows the use of AMs in a climatic context, either for climate reconstruction or for climate change impact studies. When used for future climate studies, it is necessary to pay close attention to the selected predictors, so that they contain the climate change signal. The Optimizer implements different optimization techniques, such as a sequential approach, Monte–Carlo simulation, and a global optimization technique using genetic algorithms. The process of inferring a statistical relationship between predictors and predictand is quite intensive in terms of processing because it requires numerous assessments over decades. To this end, the Optimizer was highly optimized in terms of computing efficiency, is parallelized over multiple threads and scales well on a Linux cluster. This procedure is only required to infer the statistical relationship, which can then be used in forecasting or downscaling at a low computing cost.

scholarly journals The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation

2015 ◽  
Vol 96 (8) ◽  
pp. 1311-1332 ◽  
Author(s):  
A. J. Illingworth ◽  
H. W. Barker ◽  
A. Beljaars ◽  
M. Ceccaldi ◽  
H. Chepfer ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
European Space Agency ◽  
Radiative Properties ◽  
High Spectral Resolution ◽  
Satellite Mission ◽  
Space Agency ◽  
Weather Events ◽  
Aerosol Distribution ◽  
High Impact Weather

Abstract The collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint European Space Agency (ESA)–Japan Aerospace Exploration Agency (JAXA) Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite mission, scheduled for launch in 2018, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Aqua. Specifically, EarthCARE’s cloud profiling radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle, and raindrop fall speeds. EarthCARE’s 355-nm high-spectral-resolution lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The multispectral imager will provide a context for, and the ability to construct, the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross section. The consistency of the retrievals will be assessed to within a target of ±10 W m–2 on the (10 km)2 scale by comparing the multiview broadband radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

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