scholarly journals Climate-Quality Calibration for Low Earth-Orbit Microwave Radiometry

2020 ◽  
Vol 12 (2) ◽  
pp. 241
Author(s):  
Philip Rosenkranz ◽  
William Blackwell ◽  
R. Leslie
Keyword(s):  
Radio Occultation ◽  
Microwave Radiometer ◽  
Low Earth Orbit ◽  
Radiometric Calibration ◽  
Climate Data ◽  
Climate Trends ◽  
Microwave Radiometers ◽  
Climate Studies ◽  
Unit Standard ◽  
Selection Of

Improvements in radiometric calibration are needed to achieve the desired accuracy and stability of satellite-based microwave-radiometer observations intended for the production of climate data records. Linearity, stability and traceability of measurements to an SI-unit standard should be emphasized. We suggest radiometer design approaches to achieve these objectives in a microwave calibration-reference instrument. Multi-year stability would be verified by comparison to radio-occultation measurements. Data from such an instrument could be used for climate studies and also to transfer its calibration to weather-satellite instruments. With the suitable selection of an orbit, a climatology of the diurnal variation in the measured parameters could be compiled, which would reduce uncertainties in climate trends inferred from earlier microwave radiometers over past decades.

scholarly journals Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations

2021 ◽  
Vol 13 (6) ◽  
pp. 1096
Author(s):  
Soi Ahn ◽  
Sung-Rae Chung ◽  
Hyun-Jong Oh ◽  
Chu-Yong Chung
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Northeast Asia ◽  
Low Earth Orbit ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Radiometric Calibration ◽  
Climate Data ◽  
Error Statistics ◽  
Spatiotemporal Resolution

This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records.

scholarly journals Topside Ionosphere and Plasmasphere Modelling Using GNSS Radio Occultation and POD Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1559
Author(s):  
Fabricio S. Prol ◽  
M. Mainul Hoque
Keyword(s):  
Solar Activity ◽  
Electron Density ◽  
Radio Occultation ◽  
Geomagnetic Latitude ◽  
Low Earth Orbit ◽  
Activity Level ◽  
Topside Ionosphere ◽  
Electron Content ◽  
Total Electron ◽  
Solar Activity Level

A 3D-model approach has been developed to describe the electron density of the topside ionosphere and plasmasphere based on Global Navigation Satellite System (GNSS) measurements onboard low Earth orbit satellites. Electron density profiles derived from ionospheric Radio Occultation (RO) data are extrapolated to the upper ionosphere and plasmasphere based on a linear Vary-Chap function and Total Electron Content (TEC) measurements. A final update is then obtained by applying tomographic algorithms to the slant TEC measurements. Since the background specification is created with RO data, the proposed approach does not require using any external ionospheric/plasmaspheric model to adapt to the most recent data distributions. We assessed the model accuracy in 2013 and 2018 using independent TEC data, in situ electron density measurements, and ionosondes. A systematic better specification was obtained in comparison to NeQuick, with improvements around 15% in terms of electron density at 800 km, 26% at the top-most region (above 10,000 km) and 26% to 55% in terms of TEC, depending on the solar activity level. Our investigation shows that the developed model follows a known variation of electron density with respect to geographic/geomagnetic latitude, altitude, solar activity level, season, and local time, revealing the approach as a practical and useful tool for describing topside ionosphere and plasmasphere using satellite-based GNSS data.

scholarly journals Assessing the accuracy of microwave radiometers and radio acoustic sounding systems for wind energy applications

2016 ◽  
Author(s):  
Laura Bianco ◽  
Katja Friedrich ◽  
James Wilczak ◽  
Duane Hazen ◽  
Daniel Wolfe ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Relative Humidity ◽  
Mean Absolute Error ◽  
Microwave Radiometer ◽  
Absolute Error ◽  
Acoustic Sounding ◽  
Energy Applications ◽  
Virtual Temperature ◽  
Microwave Radiometers

Abstract. To assess current remote-sensing capabilities for wind energy applications, a remote-sensing system evaluation study, called XPIA (eXperimental Planetary boundary layer Instrument Assessment), was held in the spring of 2015 at NOAA’s Boulder Atmospheric Observatory (BAO) facility. Several remote-sensing platforms were evaluated to determine their suitability for the verification and validation processes used to test the accuracy of numerical weather prediction models. The evaluation of these platforms was performed with respect to well-defined reference systems: the BAO’s 300-m tower equipped at 6 levels (50, 100, 150, 200, 250, and 300 m) with 12 sonic anemometers and 6 temperature and relative humidity sensors; and approximately 60 radiosonde launches. In this study we first employ these reference measurements to validate temperature profiles retrieved by two co-located microwave radiometers, as well as virtual temperature measured by co-located wind profiling radars equipped with radio acoustic sounding systems. Results indicate a mean absolute error in the temperature retrieved by the microwave radiometers below 1.5 °C in the lowest 5 km of the atmosphere, and a mean absolute error in the virtual temperature measured by the radio acoustic sounding systems below 0.8 °C in the layer of the atmosphere covered by these measurements (up to approximately 1.6–2 km). We also investigated the benefit of the vertical velocity applied to the speed of sound before computing the virtual temperature by the radio acoustic sounding systems. We find that using this correction frequently increases the RASS error, and that it should not be routinely applied to all data. Water vapor density profiles measured by the MWRs were also compared with similar measurements from the soundings, showing the capability of MWRs to follow the vertical profile measured by the sounding, and finding a mean absolute error below 0.5 g m−3 in the lowest 5 km of the atmosphere. However, the relative humidity profiles measured by the microwave radiometer lack the high-resolution details available from radiosonde profiles. An encouraging and significant finding of this study was that the coefficient of determination between the lapse rate measured by the microwave radiometer and the tower measurements over the tower levels between 50 and 300 m ranged from 0.76 to 0.91, proving that these remote-sensing instruments can provide accurate information on atmospheric stability conditions in the lower boundary layer.

scholarly journals On the Origin of the Standardization Sensitivity in RegEM Climate Field Reconstructions*

2008 ◽  
Vol 21 (24) ◽  
pp. 6710-6723 ◽  
Author(s):  
Jason E. Smerdon ◽  
Alexey Kaplan ◽  
Diana Chang
Keyword(s):  
Standard Deviation ◽  
Missing Values ◽  
Mean Field ◽  
Alternative Methods ◽  
Reconstruction Method ◽  
Climate Data ◽  
The Mean ◽  
Calibration Interval ◽  
Reconstruction Interval ◽  
Selection Of

Abstract The regularized expectation maximization (RegEM) method has been used in recent studies to derive climate field reconstructions of Northern Hemisphere temperatures during the last millennium. Original pseudoproxy experiments that tested RegEM [with ridge regression regularization (RegEM-Ridge)] standardized the input data in a way that improved the performance of the reconstruction method, but included data from the reconstruction interval for estimates of the mean and standard deviation of the climate field—information that is not available in real-world reconstruction problems. When standardizations are confined to the calibration interval only, pseudoproxy reconstructions performed with RegEM-Ridge suffer from warm biases and variance losses. Only cursory explanations of this so-called standardization sensitivity of RegEM-Ridge have been published, but they have suggested that the selection of the regularization (ridge) parameter by means of minimizing the generalized cross validation (GCV) function is the source of the effect. The origin of the standardization sensitivity is more thoroughly investigated herein and is shown not to be associated with the selection of the ridge parameter; sets of derived reconstructions reveal that GCV-selected ridge parameters are minimally different for reconstructions standardized either over both the reconstruction and calibration interval or over the calibration interval only. While GCV may select ridge parameters that are different from those that precisely minimize the error in pseudoproxy reconstructions, RegEM reconstructions performed with truly optimized ridge parameters are not significantly different from those that use GCV-selected ridge parameters. The true source of the standardization sensitivity is attributable to the inclusion or exclusion of additional information provided by the reconstruction interval, namely, the mean and standard deviation fields computed for the complete modeled dataset. These fields are significantly different from those for the calibration period alone because of the violation of a standard EM assumption that missing values are missing at random in typical paleoreconstruction problems; climate data are predominantly missing in the preinstrumental period when the mean climate was significantly colder than the mean of the instrumental period. The origin of the standardization sensitivity therefore is not associated specifically with RegEM-Ridge, and more recent attempts to regularize the EM algorithm using truncated total least squares could theoretically also be susceptible to the problems affecting RegEM-Ridge. Nevertheless, the principal failure of RegEM-Ridge arises because of a poor initial estimate of the mean field, and therefore leaves open the possibility that alternative methods may perform better.

The CIMR mission and its unique capabilities for soil moisture sensing

2021 ◽  
Author(s):  
Maria Piles ◽  
Roberto Fernandez-Moran ◽  
Luis Gómez-Chova ◽  
Gustau Camps-Valls ◽  
Dara Entekhabi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Microwave Radiometer ◽  
Critical Time ◽  
Global Scale ◽  
The Arctic ◽  
Temporal Perspective ◽  
Microwave Radiometers ◽  
Moisture Sensing

<p>The Copernicus Imaging Microwave Radiometer (CIMR) mission is currently being developed as a High Priority Copernicus Mission to support the Integrated European Policy for the Arctic. Due to its measurement characteristics, CIMR has exciting capabilities to enable a unique set of land surface products and science applications at a global scale. These characteristics go beyond what previous microwave radiometers (e.g. AMSR series, SMAP and SMOS) provide, and therefore allow for entirely new approaches to the estimation of bio-geophysical products from brightness temperature observations. Most notably, CIMR channels (L-,C-,X-,Ka-,Ku-bands) are very well fit for the simultaneous retrieval of soil moisture and vegetation properties, like biomass and moisture of different plant components such as leaves, stems or trunks. Also, the distinct spatial resolution of each frequency band allows for the development of approaches to cascade information and obtain these properties at multiple spatial scales. From a temporal perspective, CIMR has a higher revisit time than previous L-band missions dedicated to soil moisture monitoring (about 1 day global, sub-daily at the poles). This improved temporal resolution could allow resolving critical time scales of water processes, which is relevant to better model and understand land-atmosphere exchanges and feedbacks. In this presentation, new opportunities for soil moisture remote sensing made possible by the CIMR mission, as well as synergies and cross-sensor opportunities will be discussed.  </p>

Towards a continuous NASA cloud climate data record from MODIS to VIIRS

2021 ◽  
Author(s):  
Kerry Meyer ◽  
Steven Platnick ◽  
Robert Holz ◽  
Steven Ackerman ◽  
Andrew Heidinger ◽  
...  
Keyword(s):  
Observation Time ◽  
Radiometric Calibration ◽  
Cloud Detection ◽  
Climate Data ◽  
Cloud Data ◽  
Microphysical Properties ◽  
Data Record ◽  
Climate Data Record ◽  
Relative Radiometric Calibration

<p>The Suomi NPP and JPSS series VIIRS imagers provide an opportunity to extend the NASA EOS Terra (20+ year) and Aqua (18+ year) MODIS cloud climate data record into the new generation NOAA operational weather satellite era. However, while building a consistent, long-term cloud data record has proven challenging for the two MODIS sensors alone, the transition to VIIRS presents additional challenges due to its lack of key water vapor and CO<sub>2</sub> absorbing channels available on MODIS that are used for high cloud detection and cloud-top property retrievals, and a mismatch in the spectral location of the 2.2µm shortwave infrared channels on MODIS and VIIRS that has important implications on inter-sensor consistency of cloud optical/microphysical property retrievals and cloud thermodynamic phase. Moreover, sampling differences between MODIS and VIIRS, including spatial resolution and local observation time, and inter-sensor relative radiometric calibration pose additional challenges. To create a continuous, long-term cloud climate data record that merges the observational records of MODIS and VIIRS while mitigating the impacts of these sensor differences, a common algorithm approach was pursued that utilizes a subset of spectral channels available on each imager. The resulting NASA CLDMSK (cloud mask) and CLDPROP (cloud-top and optical/microphysical properties) products were publicly released for Aqua MODIS and SNPP VIIRS in early 2020, with NOAA-20 (JPSS-1) VIIRS following in early 2021. Here, we present an overview of the MODIS-VIIRS CLDMSK and CLDPROP common algorithm approach, discuss efforts to monitor and address relative radiometric calibration differences, and highlight early analysis of inter-sensor cloud product dataset continuity.</p>

scholarly journals Climate change impact – residential unit

2019 ◽  
Vol 279 ◽  
pp. 03007
Author(s):  
Ján Hollý ◽  
Adela Palková
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Weather Data ◽  
Emission Scenarios ◽  
Climate Data ◽  
Climate Conditions ◽  
Residential Unit ◽  
Data Application ◽  
Change Impact ◽  
Selection Of

The issue of climate change is undeniably demonstrating its presence. Consequently, there is a rising need to be prepared for upcoming threats by any means possible. One of the precautions includes obtaining the information characterizing the expected impact of global warming. This will allow authorities and other stakeholders to act accordingly in time. The article presents the assessment of the extent of impact of energy-related construction solutions in dwelling type unit situated in Central Europe region under the 21st century climate conditions. The findings represent eventual demands of energy for cooling and heating and its prospective savings. This is conducted by consecutively and automatically changing the parameters in individual simulation runs. As a basis for simulations, regionally scaled weather data of three different climate areas are used. These data are based on the emission scenarios by IPCC and are reaching to the year 2100. The selection of assessed parameters and climate data application are briefly explained in the article. The results of simulations are evaluated and recommended solutions are stated in regard to the specific energy-related construction changes. The aim is to successfully mitigate and adapt to the climate change phenomenon.

scholarly journals Whitecap Observations by Microwave Radiometers: With Discussion on Surface Roughness and Foam Contributions

2020 ◽  
Vol 12 (14) ◽  
pp. 2277
Author(s):  
Paul A. Hwang
Keyword(s):  
Surface Roughness ◽  
Dielectric Properties ◽  
Surface Wave ◽  
Wave Breaking ◽  
Microwave Radiometer ◽  
Video Recording ◽  
Ocean Surface ◽  
Optical Observations ◽  
Microwave Signals ◽  
Microwave Radiometers

Ocean surface whitecaps manifest surface wave breaking. Most of the whitecap data reported in the literature are based on optical observations through photographic or video recording. The air in whitecaps modifies the dielectric properties of microwave emissions and scattering. Therefore, whitecap information is intrinsic to microwave signals. This paper discusses a method to retrieve the ocean surface whitecap coverage from microwave radiometer signals.

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