error characterization
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2021 ◽  
Author(s):  
Tobias Böck ◽  
Bernhard Pospichal ◽  
Ulrich Löhnert

<p>The atmospheric boundary layer (ABL) is the most important under-sampled part of the atmosphere. ABL monitoring is crucial for short-range forecasting of severe weather within highly resolving numerical weather predictions (NWP). Top-priority atmospheric variables for NWP applications like temperature (T) and humidity (H) profiles are currently not adequately measured. Ground-based microwave radiometers (MWRs) like HATPRO (Humidity And Temperature PROfiler) are particularly well suited to obtain such T-profiles in the ABL as well as coarse resolution H-profiles. It has been shown by previous studies that the assimilation of MWR observations is beneficial for NWP models, however MWR data are not yet routinely assimilated into operational NWP. The HATPRO measures in zenith and other angles throughout the troposphere over an area with ~10 km radius and has a temporal resolution on the order of seconds. Measured brightness temperatures (TB) are used to retrieve the T- and H-profiles. Path integrated values IWV (Integrated Water Vapor) and LWP (Liquid Water Path) are quite reliable with excellent uncertainties up to 0.5 kg/m<sup>2</sup> and 20 g/m<sup>2</sup>, respectively.</p> <p>Driven by the E-PROFILE program, a business case proposal was recently accepted by EUMETNET to continuously provide MWR data to the European meteorological services. Also, the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases (ACTRIS) and the European COST action PROBE (PROfiling the atmospheric Boundary layer at European scale) currently focus on establishing continent-wide quality and observation standards for MWR networks for research as well as for NWP applications. The German Weather Service (DWD) also investigates the potential of HATPRO networks for improving short-term weather forecasts over Germany.</p> <p>For all this it is important to obtain an overview of what HATPROs are capable of in regard to their measurement uncertainty. This was done by conducting coordinated experiments at JOYCE (Jülich Observatory for Cloud Evolution) and the FESSTVaL (Field Experiment on Submesoscale Spatio-Temporal Variability at Lindenberg) campaign in 2021 within a prototype MWR network. The goal is to develop a standard procedure for error characterization that can be applied to any HATPRO network instrument (guidance for operators).</p> <p>Important error components are absolute calibration errors (biases), drifts (instrument stability, leaps between calibrations), radiometric noise and also location specific radio frequency interferences (RFI). For the absolute calibration with liquid nitrogen, the repeatability, the integration time and the time between calibrations are essential. Differences between consecutive calibrations are analysed, the right duration of a calibration and the right amount of time between calibrations are proposed, referring to the magnitude of the observed drifts. For the determination of noise levels for each channel, covariance matrices (correlated noise) of measured brightness temperatures on the cold- and hotload references are presented. RFI are detectable via clear-sky azimuth- and/or elevation scans.</p>


2021 ◽  
Vol 14 (12) ◽  
pp. 7775-7807
Author(s):  
Christophe Lerot ◽  
François Hendrick ◽  
Michel Van Roozendael ◽  
Leonardo M. A. Alvarado ◽  
Andreas Richter ◽  
...  

Abstract. We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite. Atmospheric glyoxal results from the oxidation of other non-methane volatile organic compounds (NMVOCs) and from direct emissions caused by combustion processes. Therefore, this product is a useful indicator of VOC emissions. It is generated with an improved version of the BIRA-IASB scientific retrieval algorithm relying on the differential optical absorption spectroscopy (DOAS) approach. Among the algorithmic updates, the DOAS fit now includes corrections to mitigate the impact of spectral misfits caused by scene brightness inhomogeneity and strong NO2 absorption. The product comes along with a full error characterization, which allows for providing random and systematic error estimates for every observation. Systematic errors are typically in the range of 1 ×1014–3 ×1014 molec. cm−2 (∼30 %–70 % in emission regimes) and originate mostly from a priori data uncertainties and spectral interferences with other absorbing species. The latter may be at the origin, at least partly, of an enhanced glyoxal signal over equatorial oceans, and further investigation is needed to mitigate them. Random errors are large (>6×1014 molec. cm−2) but can be reduced by averaging observations in space and/or time. Benefiting from a high signal-to-noise ratio and a large number of small-size observations, TROPOMI provides glyoxal tropospheric column fields with an unprecedented level of detail. Using the same retrieval algorithmic baseline, glyoxal column data sets are also generated from the Ozone Monitoring Instrument (OMI) on Aura and from the Global Ozone Monitoring Experiment-2 (GOME-2) on board Metop-A and Metop-B. Those four data sets are intercompared over large-scale regions worldwide and show a high level of consistency. The satellite glyoxal columns are also compared to glyoxal columns retrieved from ground-based Multi-AXis DOAS (MAX-DOAS) instruments at nine stations in Asia and Europe. In general, the satellite and MAX-DOAS instruments provide consistent glyoxal columns both in terms of absolute values and variability. Correlation coefficients between TROPOMI and MAX-DOAS glyoxal columns range between 0.61 and 0.87. The correlation is only poorer at one mid-latitude station, where satellite data appear to be biased low during wintertime. The mean absolute glyoxal columns from satellite and MAX-DOAS generally agree well for low/moderate columns with differences of less than 1×1014 molec. cm−2. A larger bias is identified at two sites where the MAX-DOAS columns are very large. Despite this systematic bias, the consistency of the satellite and MAX-DOAS glyoxal seasonal variability is high.


2021 ◽  
Vol 15 (11) ◽  
pp. 5241-5260
Author(s):  
Birgit Wessel ◽  
Martin Huber ◽  
Christian Wohlfart ◽  
Adina Bertram ◽  
Nicole Osterkamp ◽  
...  

Abstract. We present the generation and validation of an updated version of the TanDEM-X digital elevation model (DEM) of Antarctica: the TanDEM-X PolarDEM 90 m of Antarctica. Improvements compared to the global TanDEM-X DEM version comprise filling gaps with newer bistatic synthetic aperture radar (SAR) acquisitions of the TerraSAR-X and TanDEM-X satellites, interpolation of smaller voids, smoothing of noisy areas, and replacement of frozen or open sea areas with geoid undulations. For the latter, a new semi-automatic editing approach allowed for the delineation of the coastline from DEM and amplitude data. Finally, the DEM was transformed into the cartographic Antarctic Polar Stereographic projection with a homogeneous metric spacing in northing and easting of 90 m. As X-band SAR penetrates the snow and ice pack by several meters, a new concept for absolute height adjustment was set up that relies on areas with stable penetration conditions and on ICESat (Ice, Cloud, and land Elevation Satellite) elevations. After DEM generation and editing, a sophisticated height error characterization of the whole Antarctic continent with ICESat data was carried out, and a validation over blue ice achieved a mean vertical height error of just −0.3 m ± 2.5 m standard deviation. The filled and edited Antarctic TanDEM-X PolarDEM 90 m is outstanding due to its accuracy, homogeneity, and coverage completeness. It is freely available for scientific purposes and provides a high-resolution data set as basis for polar research, such as ice velocity, mass balance estimation, or orthorectification.


2021 ◽  
Author(s):  
Tobias Böck ◽  
Bernhard Pospichal ◽  
Ulrich Löhnert

<p>The atmospheric boundary layer (ABL) is the most important under-sampled part of the atmosphere. ABL monitoring is crucial for short-range forecasting of severe weather within highly resolving numerical weather predictions (NWP). Top-priority atmospheric variables for NWP applications like temperature (T) and humidity (H) profiles are currently not adequately measured. Ground-based microwave radiometers (MWRs) like HATPRO (Humiditiy And Temperature PROfiler) are particularly well suited to obtain T-profiles in the ABL as well as coarse resolution H-profiles; yet MWR data are not assimilated by any operational NWP system. The HATPRO measures in zenith and other angles throughout the troposphere over an area with ~10 km radius and has a temporal resolution on the order of seconds. Measured brightness temperatures (TB) are used to retrieve the T- and rudimentary H-profiles. Path integrated values like IWV (Integrated Water Vapor) and LWP (Liquid Water Path) are more reliable with excellent uncertainties up to 0.5 kg/m<sup>2</sup> and 20 g/m<sup>2</sup>, respectively.</p><p>Driven by the E-PROFILE program, a recent proposal was accepted by EUMETNET, to continuously provide suited MWR data to the European meteorological services. Also, the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases ACTRIS or the European PROBE (PROfiling the atmospheric Boundary layer at European scale) COST action currently focus on establishing continent-wide quality and observation standards for MWR networks for research as well as for NWP applications. The German Weather Service (DWD) also investigates the potential of HATPRO networks for improving short-term weather forecasts.</p><p>For all this it is important to obtain an overview of what HATPROs are capable of in regard to their measurement uncertainty. This is done by conducting coordinated experiments at JOYCE (Jülich Observatory for Cloud Evolution) and the FESSTVaL (Field Expermient on Submesoscale Spatio-Temporal Variability at Lindenberg) campaign in 2021. The goal is to develop a standard procedure for error characterization that can be applied to any HATPRO network instrument.</p><p>During FESSTVaL, there are 4 HATPROs on site which presents the unique opportunity to assess calibration procedures and measurements in order to characterize systematic errors and random uncertainties for each channel. Important error components are absolute calibration errors (biases), drifts (instrument stability, leaps between calibrations), radiometric noise and radio frequency interference. For the absolute calibration with liquid nitrogen, the repeatability, the duration, and the time between calibrations are essential. Differences between two consecutive calibrations should be minimal, the right duration of a calibration and the right amount of time between calibrations are to be defined, as are the magnitudes of the drifts. For the determination of noise levels for each channel, covariance matrices of measured brightness temperatures from the cold- and hot-load are necessary. With these matrices, the correlated noise of each channel with itself and which each other are studied.</p>


2021 ◽  
Author(s):  
Christophe Lerot ◽  
François Hendrick ◽  
Michel Van Roozendael ◽  
Leonardo M. A. Alvarado ◽  
Andreas Richter ◽  
...  

Abstract. We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the Sentinel-5 Precursor satellite. Atmospheric glyoxal results from the oxidation of other non-methane volatile organic compounds (NMVOCs) and from direct emissions caused by combustion processes. Therefore, this product is a useful indicator of VOC emissions. It is generated with an improved version of the BIRA-IASB scientific retrieval algorithm relying on the Differential Optical Absorption Spectroscopy (DOAS) approach. Among the algorithmic updates, the DOAS fit now includes corrections to mitigate the impact of spectral misfits caused by scene brightness inhomogeneity and strong NO2 absorption. The product comes along with a full error characterization, which allows providing random and systematic error estimates for every observation. Systematic errors are typically in the range of 1–3 × 1014 molec/cm2 (~30–70 % in emission regimes). Random errors are larger (> 6 × 1014 molec/cm2) but can be reduced by averaging observations in space and/or time. Benefiting from a high signal-to-noise ratio and a large number of small-size observations, TROPOMI provides glyoxal tropospheric column fields with an unprecedented level of details. Using the same retrieval algorithmic baseline, glyoxal column data sets are also generated from the Ozone Monitoring Instrument (OMI) on Aura and from the Global Ozone Monitoring Experiment-2 (GOME-2) on board of Metop-A and Metop-B. Those four data sets are intercompared over large-scale regions worldwide and show a high level of consistency. The satellite glyoxal columns are also compared to glyoxal columns retrieved from ground-based Multi-Axis (MAX-) DOAS instruments at nine stations in Asia and Europe. In general, the satellite and MAX-DOAS instruments provide consistent glyoxal columns both in terms of absolute values and variability. Correlation coefficients between TROPOMI and MAX-DOAS glyoxal columns range between 0.61 and 0.87. The correlation is only poorer at one mid-latitude station, where satellite data appears low biased during wintertime. The mean absolute glyoxal columns from satellite and MAX-DOAS generally agree well for low/moderate columns with differences less than 1 × 1014 molec/cm2. A larger bias is identified at two sites where the MAX-DOAS columns are very large. Despite this systematic bias, the consistency of the satellite and MAX-DOAS glyoxal seasonal variability is excellent.


2021 ◽  
Vol 14 (3) ◽  
pp. 1941-1957
Author(s):  
Joaquim V. Teixeira ◽  
Hai Nguyen ◽  
Derek J. Posselt ◽  
Hui Su ◽  
Longtao Wu

Abstract. Wind-tracking algorithms produce atmospheric motion vectors (AMVs) by tracking clouds or water vapor across spatial–temporal fields. Thorough error characterization of wind-tracking algorithms is critical in properly assimilating AMVs into weather forecast models and climate reanalysis datasets. Uncertainty modeling should yield estimates of two key quantities of interest: bias, the systematic difference between a measurement and the true value, and standard error, a measure of variability of the measurement. The current process of specification of the errors in inverse modeling is often cursory and commonly consists of a mixture of model fidelity, expert knowledge, and need for expediency. The method presented in this paper supplements existing approaches to error specification by providing an error characterization module that is purely data-driven. Our proposed error characterization method combines the flexibility of machine learning (random forest) with the robust error estimates of unsupervised parametric clustering (using a Gaussian mixture model). Traditional techniques for uncertainty modeling through machine learning have focused on characterizing bias but often struggle when estimating standard error. In contrast, model-based approaches such as k-means or Gaussian mixture modeling can provide reasonable estimates of both bias and standard error, but they are often limited in complexity due to reliance on linear or Gaussian assumptions. In this paper, a methodology is developed and applied to characterize error in tracked wind using a high-resolution global model simulation, and it is shown to provide accurate and useful error features of the tracked wind.


2021 ◽  
Author(s):  
Landon Rieger ◽  
Adam Bourassa ◽  
Daniel Zawada ◽  
Doug Degenstein ◽  
Sergey Khaykin ◽  
...  

<p>The eruption of Raikoke on June 22nd, 2019 was one of the largest in recent decades, spewing approximately 1.5 Tg of sulfur up to 17 km altitude. This eruption has been widely studied using a combination of climate models and measurement systems, including ground based lidars, in situ particle counters, and a variety of satellite platforms. The early plume has been well categorized by high-resolution measurements from CALIPSO, MODIS, VIIRS, IASI and other nadir viewing instruments, but as the plume ages investigation often shifts to limb sounding instruments that provide greater sensitivity to lower aerosol levels. These instruments have proven critical in understanding the long-term radiative and climatic impacts of stratospheric aerosol burdens after these explosive events, but the complexity of the measurements, sampling, and retrievals has made error characterization in high-loading conditions difficult.</p><p>This work explores systematic biases in limb measurements after the Raikoke eruption due to a variety of factors often implicit in the retrievals and analysis. Near-coincident CALIPSO, SAGE III and OMPS-LP measurements are used to investigate saturation of limb-sounding measurement in the early plume. The recent OMPS-LP v2 stratospheric aerosol product is compared with the University of Saskatchewan product to investigate benefits and drawbacks of the tomographic approach. SAGE III measurements are used as a validation when available although coverage limitations preclude comparisons in the thickest parts of the plume.  This work highlights the subtleties in comparing limb observations, with implications for model comparisons after large events such as volcanic eruptions and forest fires. Not only in the early plume, where sampling can be sparse, but also in the weeks and months following the eruption.</p>


2021 ◽  
Author(s):  
Anna Balenzano ◽  
Giuseppe Satalino ◽  
Francesco Lovergine ◽  
Davide Palmisano ◽  
Francesco Mattia ◽  
...  

<p>One of the limitations of presently available Synthetic Aperture Radar (SAR) surface soil moisture (SSM) products is their moderated temporal resolution (e.g., 3-4 days) that is non optimal for several applications, as most user requirements point to a temporal resolution of 1-2 days or less. A possible path to tackle this issue is to coordinate multi-mission SAR acquisitions with a view to the future Copernicus Sentinel-1 (C&D and Next Generation) and L-band Radar Observation System for Europe (ROSE-L).</p><p>In this respect, the recent agreement between the Japanese (JAXA) and European (ESA) Space Agencies on the use of SAR Satellites in Earth Science and Applications provides a framework to develop and validate multi-frequency and multi-platform SAR SSM products. In 2019 and 2020, to support insights on the interoperability between C- and L-band SAR observations for SSM retrieval, Sentinel-1 and ALOS-2 systematic acquisitions over the TERENO (Terrestrial Environmental Observatories) Selhausen (Germany) and Apulian Tavoliere (Italy) cal/val sites were gathered. Both sites are well documented and equipped with hydrologic networks.</p><p>The objective of this study is to investigate the integration of multi-frequency SAR measurements for a consistent and harmonized SSM retrieval throughout the error characterization of a combined C- and L-band SSM product. To this scope, time series of Sentinel-1 IW and ALOS-2 FBD data acquired over the two sites will be analysed. The short time change detection (STCD) algorithm, developed, implemented and recently assessed on Sentinel-1 data [e.g., Balenzano et al., 2020; Mattia et al., 2020], will be tailored to the ALOS-2 data. Then, the time series of SAR SSM maps from each SAR system will be derived separately and aggregated in an interleaved SSM product. Furthermore, it will be compared against in situ SSM data systematically acquired by the ground stations deployed at both sites. The study will assess the interleaved SSM product and evaluate the homogeneous quality of C- and L-band SAR SSM maps.</p><p> </p><p> </p><p>References</p><p>Balenzano. A., et al., “Sentinel-1 soil moisture at 1km resolution: a validation study”, submitted to Remote Sensing of Environment (2020).</p><p>Mattia, F., A. Balenzano, G. Satalino, F. Lovergine, A. Loew, et al., “ESA SEOM Land project on Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution,” final report, contract number 4000118762/16/I-NB, 2020.</p>


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