Impact of the revisit frequency on cloud climatology for CALIPSO, EarthCARE, Aeolus, and ICESat-2 satellite lidar missions

Author(s):
Kotarba, A.Z.
Publication title: Atmospheric Measurement Techniques
2022
 | Volume: 15 | Issue: 14
2022
DOI: 10.5194/amt-15-4307-2022
Abstract:
Space profiling lidars offer a unique insight into cloud properties in Earth's atmosphere and are considered the most reliable source of total (column… Space profiling lidars offer a unique insight into cloud properties in Earth's atmosphere and are considered the most reliable source of total (column-integrated) cloud amount (CA), and true (geometrical) cloud top height (CTH). However, lidar-based cloud climatologies suffer from infrequent sampling: every n days, and only along the ground track. This study therefore evaluated four lidar missions, namely CALIPSO (revisit every n = 16 d), EarthCARE (n = 25), Aeolus (n = 7), and ICESat-2 (n = 91), to test the hypothesis that each mission provides accurate data on CA and CTH. CA/CTH values for a hypothetical daily revisit mission were used as reference (data simulated with Meteosat 15 min cloud observations, assumed to be a proxy for ground truth). Our results demonstrated that this hypothesis is invalid, unless individual lidar transects are averaged over an area 10×10 in longitude and latitude (or larger). If this is not the case, the required accuracy of 1 % (for CA) or 150 m (for CTH) cannot be met, either for a single-year annual or monthly mean, or for a >10 year climatology. A CALIPSO-focused test demonstrated that the annual mean CA estimate is very sensitive to infrequent sampling, and that this factor alone can result in 14 % or 7 % average uncertainty with 1 or 2.5 resolution data, respectively. Consequently, applications that use gridded lidar data should consider calculating confidence intervals, or a similar measure of uncertainty. Our results suggest that CALIPSO, and its follow-on mission EarthCARE, are very likely to produce consistent cloud records despite the difference in sampling frequency. © 2022 The Author(s). more
Atmosphere CM-SAF Climate Data Records Validation

Impact of the Spatio-Temporal Mismatch Between Satellite and In Situ Measurements on Validations of Surface Solar Radiation

Author(s):
Urraca, R.; Lanconelli, C.; Gobron, N.
Publication title: Journal of Geophysical Research: Atmospheres
2024
 | Volume: 129 | Issue: 10
2024
DOI: 10.1029/2024JD041007
Abstract:
Satellite and in situ sensors do not observe exactly the same measurand. This introduces a mismatch between both types of measurements in the spatial … Satellite and in situ sensors do not observe exactly the same measurand. This introduces a mismatch between both types of measurements in the spatial or temporal. The mismatch differences can be the dominant component in their comparison, so they have to be removed for an adequate validation of satellite products. With this aim, we propose a methodology to characterize the mismatch between satellite and in situ measurements of surface solar radiation, evaluating the impact of the mismatch on validations. The Surface Solar Radiation Data Set—Heliosat (SARAH-2) and the Baseline Surface Radiation Network are used to characterize the spatial and temporal mismatch, respectively. The mismatch differences in both domains are driven by cloud variability. At least 5 years are needed to characterize the mismatch, which is not constant throughout the year due to seasonal and diurnal cloud cover patterns. Increasing the mismatch can artificially improve the validation metrics under some circumstances, but the mismatch must be always minimized for a correct product assessment. Finally, we test two types of up-scaling methods based on SARAH-2 in the validation of degree-scale products. The fully data-driven correction removes all the mismatch effects (systematic and random) but fully propagates SARAH-2 uncertainty to the corrections. The model-based correction only removes the systematic mismatch difference, but it can correct measurements not covered by the high-resolution data set and depends less SARAH-2 uncertainty. © 2024. The Authors. more
Atmosphere CM-SAF Climate Data Records Validation

Impacts of Observation Forward Operator on Infrared Radiance Data Assimilation with Fine Model Resolutions

Author(s):
Zhou, L.; Lei, L.; Tan, Z.-M.; Zhang, Y.; Di, D.
Publication title: Monthly Weather Review
2023
 | Volume: 151 | Issue: 1
2023
DOI: 10.1175/MWR-D-22-0084.1
Abstract:
All-sky radiance assimilation often has non-Gaussian observation error distributions, which can be exacerbated by high model spatial resolutions due t… All-sky radiance assimilation often has non-Gaussian observation error distributions, which can be exacerbated by high model spatial resolutions due to better resolved nonlinear physical processes. For ensemble Kalman filters, observation ensemble perturbations can be approximated by the linearized observation operator (LinHx) that uses the observation operator Jacobian of ensemble mean rather than the full observation operator (FullHx). The impact of observation operator on infrared radiance data assimilation is examined here by assimilating synthetic radiance observations from channel 1025 of GIIRS with increased model spatial resolutions from 7.5 km to 300 m. A tropical cyclone is used, while the findings are expected to be generally applied. Compared to FullHx, LinHx provides larger magnitudes of correlations and stronger corrections around observation locations, especially when all-sky radiances are assimilated at fine model resolutions. For assimilating clear-sky radiances with increasing model resolutions, LinHx has smaller errors and improved vortex intensity and structure than FullHx. But when all-sky radiances are assimilated, FullHx has advantages over LinHx. Thus, for regimes with more linearity, LinHx provides stronger correlations and imposes more corrections than FullHx; but for regimes with more nonlinearity, LinHx provides detrimental non-Gaussian prior error distributions in observation space, unrealistic correlations, and overestimated corrections, compared to FullHx. © 2023 American Meteorological Society. more
Application Atmosphere NWP-SAF Software for CDR development

Improved Use of Scatterometer Measurements by Using Stress-Equivalent Reference Winds

Author(s):
de Kloe, Jos; Stoffelen, Ad; Verhoef, Anton
Publication title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2017
 | Volume: 10 | Issue: 5
2017
DOI: 10.1109/JSTARS.2017.2685242
Abstract:
Numerical weather prediction (NWP) and buoy ocean surface winds show some systematic differences with satellite scatterometer and radiometer wind meas… Numerical weather prediction (NWP) and buoy ocean surface winds show some systematic differences with satellite scatterometer and radiometer wind measurements, both in statistical results and in local geographical regions. It is possible to rescale these reference winds to remove certain aspects of these systematic differences. Space-borne ocean surface winds actually measure ocean surface roughness, which is related more directly to stress. Air mass density is relevant in the air-sea momentum transfer as captured in the stress vector. Therefore, apart from the already common “neutral wind correction” for atmospheric stratification, also a “mass density wind correction” is investigated here to obtain a better correspondence between satellite stress measurements and buoy or NWP winds. The bicorrected winds are called stress-equivalent winds. Stress-equivalent winds do not strongly depend on the drag formulation used and provide a rather direct standard for comparison and assimilation in user applications. This paper presents details on how this correction is performed and first results that show the benefits of this correction mainly in the extratropical regions. more
Atmosphere Climate Data Records Method OSI-SAF

Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data

Author(s):
Graf, M.; Wagner, A.; Polz, J.; Lliso, L.; Lahuerta, J.A.; Kunstmann, H.; Chwala, C.
Publication title: Atmospheric Measurement Techniques
2024
 | Volume: 17 | Issue: 7
2024
DOI: 10.5194/amt-17-2165-2024
Abstract:
The most reliable areal precipitation estimation is usually generated via combinations of different measurements. Path-averaged rainfall rates can be … The most reliable areal precipitation estimation is usually generated via combinations of different measurements. Path-averaged rainfall rates can be derived from commercial microwave links (CMLs), where attenuation of the emitted radiation is strongly related to rainfall rate. CMLs can be combined with data from other rainfall measurements or can be used individually. They are available almost worldwide and often represent the only opportunity for ground-based measurement in data-scarce regions. However, deriving rainfall estimates from CML data requires extensive data processing. The separation of the attenuation time series into rainy and dry periods (rain event detection) is the most important step in this processing and has a high impact on the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. We used data from 3748 CMLs in Germany for 4 months in the summer of 2021 and data from the two SEVIRI-derived products PC and PC-Ph. We analyzed all rain event detection methods for different rainfall intensities, differences between day and night, and their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW was used for validation. The results showed that both SEVIRI products are promising candidates for ADB rainfall detection, yielding only slightly worse results than the TSB methods, with the main advantage that the ADB method does not rely on extensive validation for different CML datasets. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night due to the reduced availability of SEVIRI channels at night. In general, the ADB methods led to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations improved the Matthews correlation coefficient of the rain event detection from 0.49 (or 0.51) to 0.59 during the day and from 0.41 (or 0.50) to 0.55 during the night. Additionally, these combinations increased the number of true-positive classifications, especially for light rainfall compared to the TSB methods, and reduced the number of false negatives while only leading to a slight increase in false-positive classifications. Our results show that utilizing MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods. While the improvement is useful even for applications in Germany, we see the main potential of using ADB methods in data-scarce regions like West Africa where extensive validation is not possible. © Author(s) 2024. more
Application Atmosphere NWC-SAF Software for CDR development

Improved Rain Screening for Ku-Band Wind Scatterometry

Author(s):
Xu, Xingou; Stoffelen, Ad
Publication title: IEEE Transactions on Geoscience and Remote Sensing
2020
 | Volume: 58 | Issue: 4
2020
DOI: 10.1109/TGRS.2019.2951726
Abstract:
Spaceborne scatterometers for ocean surface winds usually operate in Ku- or C-band. Rather strict quality control (QC) procedures are included in the … Spaceborne scatterometers for ocean surface winds usually operate in Ku- or C-band. Rather strict quality control (QC) procedures are included in the Ku-band wind retrieval chain for labeling rain-contaminated observations. Existing QC factors represent the deviation of measurements from the wind geophysical model function (GMF) modeled measurement surface. Other QC indicators flag outliers by examining neighborhood consistency. In this article, spatial heterogeneity of rain is further exploited by a new indicator for Ku-band QC, namely, JOSS, the speed component of the observation cost function, JO, of the selected solution (JOS) in the 2-D variational ambiguity removal (2-DVAR) step of the wind retrieval. First, the characteristics of 2-DVAR speeds in rainy condition are analyzed, and then, the ability of JOSS in quality labeling is proposed and verified by applying it to the Ku-band scatterometer on-board ScatSat. Its effectiveness for rain screening is confirmed with collocated references from the C-band scatterometer on-board the MetOp-B satellite, which are much less affected by rain. With reference to collocated rain rates from the Global Precipitation Mission (GPM), the more direct relations to rain and wind speed errors of the newly proposed QC indicator JOSS than existing QC indicators, including JOS, are illustrated by the analysis of its correlation with rain rates. In a novel approach, JOSS is applied to accept (unflag) more than 75% of the data rejected by the widely applied maximum likelihood estimation (MLE) thresholds (i.e., correct false alarms) in the tropics. The promising results open a new opportunity for improving QC of rain in the Ku-band wind scatterometry benefitting scatterometer applications. more
Atmosphere Climate Data Records OSI-SAF Validation

Improving ASCAT Soil Moisture Retrievals With an Enhanced Spatially Variable Vegetation Parameterization

Author(s):
Hahn, Sebastian; Wagner, Wolfgang; Steele-Dunne, Susan C.; Vreugdenhil, Mariette; Melzer, Thomas
Publication title: IEEE Transactions on Geoscience and Remote Sensing
2020
2020
DOI: 10.1109/TGRS.2020.3041340
Abstract:
This study investigates the performance of the TU Wien soil moisture retrieval (TUW-SMR) algorithm by adapting the strength of the vegetation correcti… This study investigates the performance of the TU Wien soil moisture retrieval (TUW-SMR) algorithm by adapting the strength of the vegetation correction. The semiempirical change detection method TUW-SMR exploits the multiangle backscatter observations from spaceborne fan-beam scatterometer systems in order to derive surface soil moisture information expressed in the degree of saturation. The vegetation parameterization of TUW-SMR is controlled by the dry and wet crossover angles that are used to determine the dry and wet backscatter reference. Backscatter observations from the Advanced Scatterometer (ASCAT) are used to produce four soil moisture data sets based on different dry and wet crossover angles describing: 1) a static, respectively, no vegetation correction; 2) the currently used seasonal vegetation correction; 3) a stronger seasonal vegetation correction; and 4) a spatially variable seasonal vegetation correction with the stronger vegetation correction over vegetated areas and no vegetation correction over bare land. All four ASCAT soil moisture data sets are evaluated against soil moisture estimates from GLDAS-2.1 Noah land surface model and the European Space Agency (ESA) climate change initiative (CCI) Passive v04.5 soil moisture product using the triple collocation method and traditional correlation analysis. The results show that the spatially variable vegetation correction overall improves soil moisture estimates in both more densely vegetated areas, e.g., in large parts of North America and Europe, and more sparsely vegetated, e.g., Western Africa. Nonetheless, the experiment also provides insight into challenging retrieval conditions where the TUW-SMR fails to take all relevant backscatter processes into account, e.g., wetlands and bare soils with subsurface scattering. more
Climate Data Records H-SAF Land Method

Improving the Modeled Variability Estimates of Offshore Winds in Northern Europe by Nudging ASCAT-Derived Winds

Author(s):
Alonso-De-linaje, N.G.; Hahmann, A.N.; Karagali, I.; Dimitriadou, K.; Badger, M.
Publication title: Journal of Applied Meteorology and Climatology
2024
 | Volume: 63 | Issue: 7
2024
DOI: 10.1175/JAMC-D-23-0102.1
Abstract:
The paper aims to demonstrate how to enhance the accuracy of offshore wind resource estimation, specifically by incorporating near-surface satellite-d… The paper aims to demonstrate how to enhance the accuracy of offshore wind resource estimation, specifically by incorporating near-surface satellite-derived wind observations into mesoscale models. We utilized the Weather Research and Forecasting (WRF) Model and applied observational nudging by integrating ASCAT data over offshore areas to achieve this. We then evaluated the accuracy of the nudged WRF Model simulations by comparing them with data from ocean oil platforms, tall masts, and a wind lidar mounted on a commercial ferry crossing the southern Baltic Sea. Our findings indicate that including satellite-derived ASCAT wind speeds through nudging enhances the correlation and reduces the error of the mesoscale simulations across all validation platforms. Moreover, it consistently outperforms the control and previously published WRF-based wind atlases. Using satellite-derived winds directly in the model simulations also solves the issue of lifting near-surface winds to wind turbine heights, which has been challenging in estimating wind resources at such heights. The comparison of the 1-yr-long simulations with and without nudging reveals intriguing differences in the sign and magnitude between the Baltic and North Seas, which vary seasonally. The pattern highlights a distinct regional pattern at-tributed to regional dynamics, sea surface temperature, atmospheric stability, and the number of available ASCAT samples. © 2024 American Meteorological Society. more
Application Atmosphere Climate Data Records OSI-SAF

Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: Implementation and examples

Author(s):
Deneke, H.; Barrientos-Velasco, C.; Bley, S.; Hunerbein, A.; Lenk, S.; Macke, A.; Meirink, J.F.; Schroedter-Homscheidt, M.; Senf, F.; Wang, P.; Werner, F.; Witthuhn, J.
Publication title: Atmospheric Measurement Techniques
2021
 | Volume: 14 | Issue: 7
2021
DOI: 10.5194/amt-14-5107-2021
Abstract:
The modification of an existing cloud property retrieval scheme for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the… The modification of an existing cloud property retrieval scheme for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary Meteosat satellites is described to utilize its high-resolution visible (HRV) channel for increasing the spatial resolution of its physical outputs. This results in products with a nadir spatial resolution of 1×1ĝ€¯km2 compared to the standard 3×3ĝ€¯km2 resolution offered by the narrowband channels. This improvement thus greatly reduces the resolution gap between current geostationary and polar-orbiting meteorological satellite imagers. In the first processing step, cloudiness is determined from the HRV observations by a threshold-based cloud masking algorithm. Subsequently, a linear model that links the 0.6ĝ€¯μm, 0.8ĝ€¯μm, and HRV reflectances provides a physical constraint to incorporate the spatial high-frequency component of the HRV observations into the retrieval of cloud optical depth. The implementation of the method is described, including the ancillary datasets used. It is demonstrated that the omission of high-frequency variations in the cloud-absorbing 1.6ĝ€¯μm channel results in comparatively large uncertainties in the retrieved cloud effective radius, likely due to the mismatch in channel resolutions. A newly developed downscaling scheme for the 1.6ĝ€¯μm reflectance is therefore applied to mitigate the effects of this scale mismatch. Benefits of the increased spatial resolution of the resulting SEVIRI products are demonstrated for three example applications: (i) for a convective cloud field, it is shown that significantly better agreement between the distributions of cloud optical depth retrieved from SEVIRI and from collocated MODIS observations is achieved. (ii) The temporal evolution of cloud properties for a growing convective storm at standard and HRV spatial resolutions are compared, illustrating an improved contrast in growth signatures resulting from the use of the HRV channel. (iii) An example of surface solar irradiance, determined from the retrieved cloud properties, is shown, for which the HRV channel helps to better capture the large spatiotemporal variability induced by convective clouds. These results suggest that incorporating the HRV channel into the retrieval has potential for improving Meteosat-based cloud products for several application domains. © Copyright: more
Atmosphere CM-SAF Climate Data Records Method
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