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Wen Chen, Rachel T. Pinker, Yingtao Ma, Glynn Hulley, Eva Borbas, Tanvir Islam, Kerry-A. Cawse-Nicholson, Simon Hook, Chris Hain, and Jeff Basara

ABSTRACT

Land surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences between satellites in areas of overlap. The mean and median values of the differences in monthly mean LST retrieved from GOES-E and GOES-W at 0600 UTC for July are 0.01 and 0.11 K, respectively. Similarly, at 1800 UTC the respective mean and median value of the differences were 0.15 and 1.33 K. These findings can provide guidelines for potential users to decide whether the reported accuracy based on one satellite alone, meets their needs in area of overlap. Since the 6 yr record of LST was produced at hourly time scale, the data are well suited to address scientific issues that require the representation of LST diurnal cycle or the diurnal temperature range (DTR).

Open access
Florian Le Guillou, Sammy Metref, Emmanuel Cosme, Clément Ubelmann, Maxime Ballarotta, Julien Le Sommer, and Jacques Verron

Abstract

During the past 25 years, altimetric observations of the ocean surface from space have been mapped to provide two dimensional sea surface height (SSH) fields that are crucial for scientific research and operational applications. The SSH fields can be reconstructed from conventional altimetric data using temporal and spatial interpolation. For instance, the standard Developing Use of Altimetry for Climate Studies (DUACS) products are created with an optimal interpolation method that is effective for both low temporal and low spatial resolution. However, the upcoming next-generation SWOT mission will provide very high spatial resolution but with low temporal resolution. The present paper makes the case that this temporal–spatial discrepancy induces the need for new advanced mapping techniques involving information on the ocean dynamics. An algorithm is introduced, dubbed the BFN-QG, that uses a simple data assimilation method, the back-and-forth nudging (BNF), to interpolate altimetric data while respecting quasigeostrophic (QG) dynamics. The BFN-QG is tested in an observing system simulation experiments and compared to the DUACS products. The experiments consider as reference the high-resolution numerical model simulation NATL60 from which are produced realistic data: four conventional altimetric nadirs and SWOT data. In a combined nadirs and SWOT scenario, the BFN-QG substantially improves the mapping by reducing the root-mean-square errors and increasing the spectral effective resolution by 40 km. Also, the BFN-QG method can be adapted to combine large-scale corrections from nadir data and small-scale corrections from SWOT data so as to reduce the impact of SWOT correlated noises and still provide accurate SSH maps.

Open access
Petar Bukovčić, Alexander V. Ryzhkov, and Jacob T. Carlin

Abstract

The intrinsic uncertainty of radar-based retrievals in snow originates from a large diversity of snow growth habits, densities, and particle size distributions, all of which can make interpreting radar measurements of snow very challenging. The application of polarimetric radar for snow measurements can mitigate some of these issues. In this study, a novel polarimetric method for quantification of the extinction coefficient and visibility in snow, based on the joint use of radar reflectivity at horizontal polarization Z and specific differential phase K DP, is introduced. A large 2D-video-disdrometer snow dataset from central Oklahoma is used to derive a polarimetric bivariate power-law relation for the extinction coefficient, σe(KDP,Z)=γKDPαZβ. The relation is derived for particle aspect ratios ranging from 0.5 to 0.8 and the width of the canting angle distribution ranging from 0° to 40°, values typical of aggregated snow, and validated via theoretical and analytical derivations/simulations. The multiplier of the relation is sensitive to variations in particles’ densities, the width of the canting angle distribution, and particles’ aspect ratios, whereas the relation’s exponents are practically invariant to changes in the latter two parameters. This novel approach is applied to polarimetric S-band WSR-88D data and verified against previous studies and in situ measurements of the extinction coefficient for four snow events in the eastern United States. The polarimetric radar estimates of the extinction coefficient exhibit smaller biases in comparison to previous studies concerning the ground measurements. The results indicate that there is good potential for reliable radar estimates of visibility from polarimetric weather radars, a parameter inversely proportional to the extinction coefficient.

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Yi Luo, Xudong Liang, Gang Wang, and Zheng Cao

Abstract

In this study, we propose a new way to obtain motion vectors using the integrating velocity–azimuth process (IVAP) method for extrapolation nowcasting. Traditional tracking methods rely on tracking radar echoes of a few time slices. In contrast, the IVAP method does not depend on the past variation of radar echoes; it only needs the radar echo and radial velocity observations at the latest time. To demonstrate it is practical to use IVAP-retrieved winds to extrapolate radar echoes, we carried out nowcasting experiments using the IVAP method, and compared these results with the results using a traditional method, namely, the tracking radar echoes by correlation (TREC) method. Comparison based on a series of large-scale mature rainfall cases showed that the IVAP method has similar accuracy to that of the TREC method. In addition, the IVAP method provides the vertical wind profile that can be used to anticipate storm type and motion deviations.

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Mengnan Zhao, Rui M. Ponte, Ou Wang, and Rick Lumpkin

Abstract

Properly fitting ocean models to observations is crucial for improving model performance and understanding ocean dynamics. Near-surface velocity measurements from the Global Drifter Program (GDP) contain valuable information about upper-ocean circulation and air–sea fluxes on various space and time scales. This study explores whether GDP measurements can be used for usefully constraining the surface circulation from coarse-resolution ocean models, using global solutions produced by the consortium for Estimating the Circulation and Climate of the Ocean (ECCO) as an example. To address this problem, a careful examination of velocity data errors is required. Comparisons between an ECCO model simulation, performed without any data constraints, and GDP and Ocean Surface Current Analyses Real-Time (OSCAR) velocity data, over the period 1992–2017, reveal considerable differences in magnitude and pattern. These comparisons are used to estimate GDP data errors in the context of the time-mean and time-variable surface circulations. Both instrumental errors and errors associated with limitations in model physics and resolution (representation errors) are considered. Given the estimated model–data differences, errors, and signal-to-noise ratios, our results indicate that constraining ocean-state estimates to GDP can have a substantial impact on the ECCO large-scale time-mean surface circulation over extensive areas. Impact of GDP data constraints on the ECCO time-variable circulation would be weaker and mainly limited to low latitudes. Representation errors contribute substantially to degrading the data impacts.

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Hyun Mee Kim and Dae-Hui Kim

Abstract

In this study, the effect of boundary condition configurations in the regional Weather Research and Forecasting (WRF) model on the adjoint-based forecast sensitivity observation impact (FSOI) for 24 h forecast error reduction was evaluated. The FSOI has been used to diagnose the impact of observations on the forecast performance in several global and regional models. Different from the global model, in the regional model, the lateral boundaries affect forecasts and FSOI results. Several experiments with different lateral boundary conditions were conducted. The experimental period was from 1 to 14 June 2015. With or without data assimilation, the larger the buffer size in lateral boundary conditions, the smaller the forecast error. The nonlinear and linear forecast error reduction (i.e., observation impact) decreased as the buffer size increased, implying larger impact of lateral boundaries and smaller observation impact on the forecast error. In all experiments, in terms of observation types (variables), upper-air radiosonde observations (brightness temperature) exhibited the greatest observation impact. The ranking of observation impacts was consistent for observation types and variables among experiments with a constraint in the response function at the upper boundary. The fractions of beneficial observations were approximately 60%, and did not considerably vary depending on the boundary conditions specified when calculating the FSOI in the regional modeling framework.

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Mohar Chattopadhyay, Will McCarty, and Isaac Moradi

Abstract

Microwave temperature sounders provide key observations in data assimilation, both in the current and historical global observing systems, as they provide the largest amount of horizontal and vertical temperature information due to their insensitivity to clouds. In the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), microwave sounder radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) are assimilated beginning with NOAA-15 and continuing through the current period. The time series of observation minus background statistics for AMSU-A channels sensitive to the upper stratosphere and lower mesosphere show variabilities due to changes in the AMSU-A constellation in the early AMSU-A period. Noted discrepancies are seen at the onset and exit of AMSU-A observations on the NOAA-15, NOAA-16, NOAA-17, and NASA EOS Aqua satellites. This effort characterizes the sensitivity, both in terms of the observations and the MERRA-2 data. Furthermore, it explores the use of reprocessed and intercalibrated datasets to evaluate whether these homogenized observations can reduce the disparity due to change in instrumental biases against the model background. The results indicate that the AMSU-A radiances used in MERRA-2 are the fundamental cause of this interplatform sensitivity, which can be mitigated by using reprocessed data. The results explore the importance of the reprocessing of the AMSU-A radiances as well as their intercalibration.

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Wenfeng Lai, Jianping Gan, Ye Liu, Zhiqiang Liu, Jiping Xie, and Jiang Zhu

Abstract

To improve the forecasting performance in dynamically active coastal waters forced by winds, tides, and river discharges in a coupled estuary–shelf model off Hong Kong, a multivariable data assimilation (DA) system using the ensemble optimal interpolation method has been developed and implemented. The system assimilates the conductivity–temperature–depth (CTD) profilers, time series buoy measurement, and remote sensing sea surface temperature (SST) data into a high-resolution estuary–shelf ocean model around Hong Kong. We found that the time window selection associated with the local dynamics and the number of observation samples are two key factors in improving assimilation in the unique estuary–shelf system. DA with a varied assimilation time window that is based on the intratidal variation in the local dynamics can reduce the errors in the estimation of the innovation vector caused by the model–observation mismatch at the analysis time and improve simulation greatly in both the estuary and coastal regions. Statistically, the overall root-mean-square error (RMSE) between the DA forecasts and not-yet-assimilated observations for temperature and salinity has been reduced by 33.0% and 31.9% in the experiment period, respectively. By assimilating higher-resolution remote sensing SST data instead of lower-resolution satellite SST, the RMSE of SST is improved by ~18%. Besides, by assimilating real-time buoy mooring data, the model bias can be continuously corrected both around the buoy location and beyond. The assimilation of the combined buoy, CTD, and SST data can provide an overall improvement of the simulated three-dimensional solution. A dynamics-oriented assimilation scheme is essential for the improvement of model forecasting in the estuary–shelf system under multiple forcings.

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F. Tornow, C. Domenech, J. N. S. Cole, N. Madenach, and J. Fischer

Abstract

Top-of-atmosphere (TOA) shortwave (SW) angular distribution models (ADMs) approximate—per angular direction of an imagined upward hemisphere—the intensity of sunlight scattered back from a specific Earth–atmosphere scene. ADMs are, thus, critical when converting satellite-borne broadband radiometry into estimated radiative fluxes. This paper applies a set of newly developed ADMs with a more refined scene definition and demonstrates tenable changes in estimated fluxes compared to currently operational ADMs. Newly developed ADMs use a semiphysical framework to consider cloud-top effective radius (R¯e) and above-cloud water vapor (ACWV), in addition to accounting for surface wind speed and clouds’ phase, fraction, and optical depth. In effect, instantaneous TOA SW fluxes for marine liquid-phase clouds had the largest flux differences (of up to 25 W m−2) for lower solar zenith angles and cloud optical depth greater than 10 due to extremes in R¯e or ACWV. In regions where clouds had persistently extreme levels of R¯e (here mostly for R¯e<7μm and R¯e>15μm) or ACWV, instantaneous fluxes estimated from Aqua, Terra, Meteosat-8, and Meteosat-9 satellites using the two ADMs differed systematically, resulting in significant deviations in daily mean fluxes (up to ±10 W m−2) and monthly mean fluxes (up to ±5 W m−2). Flux estimates using newly developed, semiphysical ADMs may contribute to a better understanding of solar fluxes over low-level clouds. It remains to be seen whether aerosol indirect effects are impacted by these updates.

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Corey G. Amiot, Sayak K. Biswas, Timothy J. Lang, and David I. Duncan

Abstract

Recent upgrades, calibration, and scan-angle bias reductions to the Advanced Microwave Precipitation Radiometer (AMPR) have yielded physically realistic brightness temperatures (T b) from the Olympic Mountains Experiment and Radar Definition Experiment (OLYMPEX/RADEX) dataset. Measured mixed-polarization T b were converted to horizontally and vertically polarized T b via dual-polarization deconvolution, and linear regression equations were developed to retrieve integrated cloud liquid water (CLW), water vapor (WV), and 10-m wind speed (WS) using simulated AMPR T b and modeled atmospheric profiles. These equations were tested using AMPR T b collected during four OLYMPEX/RADEX cases; the resulting geophysical values were compared with independent retrieval (1DVAR) results from the same dataset, while WV and WS were also compared with in situ data. Geophysical calculations using simulated T b yielded relatively low retrieval and crosstalk errors when compared with modeled profiles; average CLW, WV, and WS root-mean-square deviations (RMSD) were 0.11 mm, 1.28 mm, and 1.11 m s−1, respectively, with median absolute deviations (MedAD) of 2.26 × 10−2 mm, 0.22 mm, and 0.55 m s−1, respectively. When applied to OLYMPEX/RADEX data, the new retrieval equations compared well with 1DVAR; CLW, WV, and WS RMSD were 9.95 × 10−2 mm, 2.00 mm, and 2.35 m s−1, respectively, and MedAD were 2.88 × 10−2 mm, 1.14 mm, and 1.82 m s−1, respectively. WV MedAD between the new equations and dropsondes were 2.10 and 1.80 mm at the time and location of minimum dropsonde altitude, respectively, while WS MedAD were 1.15 and 1.53 m s−1, respectively, further indicating the utility of these equations.

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