scholarly journals Uncertainty Characterization of HOAPS-3.3 Latent Heat Flux Related Parameters

2017 ◽  
Author(s):  
Julian Kinzel ◽  
Marc Schröder ◽  
Karsten Fennig ◽  
Axel Andersson ◽  
Rainer Hollmann
Keyword(s):  
Latent Heat ◽  
Heat Fluxes ◽  
Specific Humidity ◽  
Boreal Winter ◽  
Western Boundary ◽  
Near Surface ◽  
In Situ Data ◽  
Uncertainty Measures ◽  
Global Mean

Abstract. Latent heat fluxes (LHF) are one of the main contributors to the global energy budget. As the density of LHF measurements over the global oceans is generally poor, the potential of remotely sensed LHF for meteorological applications is enormous. However, to date none of the available satellite products include estimates of systematic, random retrieval, and sampling uncertainties, all of which are essential for assessing their quality. Here, this challenge is taken on by applying regionally independent multi-dimensional bias analyses to LHF-related parameters (wind speed U, near-surface specific humidity qa, and sea surface saturation specific humidity qs) of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) climatology. In connection with multiple triple collocation analyses, it is demonstrated how both instantaneous (gridded) uncertainty measures may be assigned to each pixel (grid box). A high-quality in situ data archive including buoys and selected ships serves as the ground reference. Results show that systematic LHF uncertainties range between 15–50 W m-2 with a global mean of 25 W m-2. Local maxima are mainly found over the subtropical ocean basins as well as along the western boundary currents. Investigations indicate that contributions by qa (U) to the overall LHF uncertainty are in the order of 60 % (25 %). From an instantaneous point of view, random retrieval uncertainties are specifically large over the subtropics with a global average of 37 W m-2. In a climatological sense, their magnitudes become negligible, as do respective sampling uncertainties. Time series analyses show footprints of climate events, such as the strong El Niño during 1997/98. Regional and seasonal analyses suggest that largest total (i.e., systematic + instantaneous random) LHF uncertainties are seen over the Gulf Stream and the Indian monsoon region during boreal winter. In light of the uncertainty measures, the observed continuous global mean LHF increase up to 2009 needs to be treated with caution. First intercomparisons to other LHF climatologies (in situ, satellite) reveal overall resemblance with few, yet distinct exceptions.

scholarly journals Uncertainty characterization of HOAPS 3.3 latent heat-flux-related parameters

2018 ◽  
Vol 11 (3) ◽  
pp. 1793-1815 ◽  
Author(s):  
Julian Liman ◽  
Marc Schröder ◽  
Karsten Fennig ◽  
Axel Andersson ◽  
Rainer Hollmann
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Quality Data ◽  
Boreal Winter ◽  
Western Boundary ◽  
Decomposition Approach ◽  
In Situ Data ◽  
Global Mean

Abstract. Latent heat flux (LHF) is one of the main contributors to the global energy budget. As the density of in situ LHF measurements over the global oceans is generally poor, the potential of remotely sensed LHF for meteorological applications is enormous. However, to date none of the available satellite products have included estimates of systematic, random, and sampling uncertainties, all of which are essential for assessing their quality. Here, the challenge is taken on by matching LHF-related pixel-level data of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) climatology (version 3.3) to in situ measurements originating from a high-quality data archive of buoys and selected ships. Assuming the ground reference to be bias-free, this allows for deriving instantaneous systematic uncertainties as a function of four atmospheric predictor variables. The approach is regionally independent and therefore overcomes the issue of sparse in situ data densities over large oceanic areas. Likewise, random uncertainties are derived, which include not only a retrieval component but also contributions from in situ measurement noise and the collocation procedure. A recently published random uncertainty decomposition approach is applied to isolate the random retrieval uncertainty of all LHF-related HOAPS parameters. It makes use of two combinations of independent data triplets of both satellite and in situ data, which are analysed in terms of their pairwise variances of differences. Instantaneous uncertainties are finally aggregated, allowing for uncertainty characterizations on monthly to multi-annual timescales. Results show that systematic LHF uncertainties range between 15 and 50 W m−2 with a global mean of 25 W m−2. Local maxima are mainly found over the subtropical ocean basins as well as along the western boundary currents. Investigations indicate that contributions from qa (U) to the overall LHF uncertainty are on the order of 60 % (25 %). From an instantaneous point of view, random retrieval uncertainties are specifically large over the subtropics with a global average of 37 W m−2. In a climatological sense, their magnitudes become negligible, as do respective sampling uncertainties. Regional and seasonal analyses suggest that largest total LHF uncertainties are seen over the Gulf Stream and the Indian monsoon region during boreal winter. In light of the uncertainty measures, the observed continuous global mean LHF increase up to 2009 needs to be treated with caution. The demonstrated approach can easily be transferred to other satellite retrievals, which increases the significance of the present work.

scholarly journals Decomposition of Random Errors Inherent to HOAPS-3.2 Near-Surface Humidity Estimates Using Multiple Triple Collocation Analysis

2016 ◽  
Vol 33 (7) ◽  
pp. 1455-1471 ◽  
Author(s):  
Julian Kinzel ◽  
Karsten Fennig ◽  
Marc Schröder ◽  
Axel Andersson ◽  
Karl Bumke ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Large Scale ◽  
Heat Fluxes ◽  
Specific Humidity ◽  
Random Errors ◽  
Bias Analysis ◽  
Data Set ◽  
Near Surface ◽  
Ocean Atmosphere

AbstractLatent heat fluxes (LHF) play an essential role in the global energy budget and are thus important for understanding the climate system. Satellite-based remote sensing permits a large-scale determination of LHF, which, among others, are based on near-surface specific humidity . However, the random retrieval error () remains unknown. Here, a novel approach is presented to quantify the error contributions to pixel-level of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data, version 3.2 (HOAPS, version 3.2), dataset. The methodology makes use of multiple triple collocation (MTC) analysis between 1995 and 2008 over the global ice-free oceans. Apart from satellite records, these datasets include selected ship records extracted from the Seewetteramt Hamburg (SWA) archive and the International Comprehensive Ocean–Atmosphere Data Set (ICOADS), serving as the in situ ground reference. The MTC approach permits the derivation of as the sum of model uncertainty and sensor noise , while random uncertainties due to in situ measurement errors () and collocation () are isolated concurrently. Results show an average of 1.1 ± 0.3 g kg−1, whereas the mean () is in the order of 0.5 ± 0.1 g kg−1 (0.5 ± 0.3 g kg−1). Regional analyses indicate a maximum of exceeding 1.5 g kg−1 within humidity regimes of 12–17 g kg−1, associated with the single-parameter, multilinear retrieval applied in HOAPS. Multidimensional bias analysis reveals that global maxima are located off the Arabian Peninsula.

scholarly journals Estimation of Surface Air Specific Humidity and Air–Sea Latent Heat Flux Using FY-3C Microwave Observations

2019 ◽  
Vol 11 (4) ◽  
pp. 466 ◽  
Author(s):  
Qidong Gao ◽  
Sheng Wang ◽  
Xiaofeng Yang
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Microwave Radiometer ◽  
Sea Surface ◽  
Specific Humidity ◽  
Near Surface ◽  
In Situ Data

Latent heat flux (LHF) plays an important role in the global hydrological cycle and is therefore necessary to understand global climate variability. It has been reported that the near-surface specific humidity is a major source of error for satellite-derived LHF. Here, a new empirical model relating multichannel brightness temperatures ( T B ) obtained from the Fengyun-3 (FY-3C) microwave radiometer and sea surface air specific humidity ( Q a ) is proposed. It is based on the relationship between T B , Q a , sea surface temperature (SST), and water vapor scale height. Compared with in situ data, the new satellite-derived Q a and LHF both exhibit better statistical results than previous estimates. For Q a , the bias, root mean square difference (RMSD), and the correlation coefficient (R2) between satellite and buoy in the mid-latitude region are 0.08 g/kg, 1.76 g/kg, and 0.92, respectively. For LHF, the bias, RMSD, and R2 are 2.40 W/m2, 34.24 W/m2, and 0.87, respectively. The satellite-derived Q a are also compared with National Oceanic and Atmospheric Administration (NOAA) Cooperative Institute for Research in Environmental Sciences (CIRES) humidity datasets, with a bias, RMSD, and R2 of 0.02 g/kg, 1.02 g/kg, and 0.98, respectively. The proposed method can also be extended in the future to observations from other space-borne microwave radiometers.

scholarly journals A New Technique for Estimation of Surface Latent Heat Fluxes Using Satellite-Based Observations

2005 ◽  
Vol 133 (9) ◽  
pp. 2692-2710 ◽  
Author(s):  
Randhir Singh ◽  
P. C. Joshi ◽  
C. M. Kishtawal
Keyword(s):  
Latent Heat ◽  
Precipitable Water ◽  
Heat Fluxes ◽  
Specific Humidity ◽  
Data Set ◽  
Near Surface ◽  
Microwave Imager ◽  
A New Technique ◽  
Global Oceans ◽  
Globally Distributed

Abstract Monthly mean surface latent heat fluxes (LHFs) over the global oceans are estimated using bulk formula. LHFs are computed using wind speed (U) from the Special Sensor Microwave Imager (SSM/I), sea surface temperature (SST) from the Advanced Very High Resolution Radiometer (AVHRR), and near-surface specific humidity. Near-surface specific humidity (Qa) is estimated from SSM/I-observed precipitable water (W) and AVHRR-observed SST using a genetic algorithm (GA) approach. The GA-retrieved monthly mean Qa has an accuracy of 0.80 ± 0.32 g kg−1 as compared with surface marine observations based on the Comprehensive Ocean–Atmosphere Data Set (COADS). The GA approach improves upon the surface specific humidity retrieval based on regression, the EOF approach, and is comparable to the artificial neural network technique. The satellite-derived LHFs are compared with globally distributed surface marine observations to monthly averages of 1° × 1° latitude–longitude bins, during 1988–93. When GA-retrieved Qa is used in the computation of satellite-derived latent heat fluxes (LHFGA) the global mean rmse, bias, and correlation are 22 ± 8 W m−2, 5 W m−2, and 0.85, respectively, for monthly mean latent heat fluxes. The rmses in LHF are larger when Qa is retrieved using regression and EOF approaches.

scholarly journals Air-Sea Interactions over Eddies in the Brazil-Malvinas Confluence

2021 ◽  
Vol 13 (7) ◽  
pp. 1335
Author(s):  
Ronald Souza ◽  
Luciano Pezzi ◽  
Sebastiaan Swart ◽  
Fabrício Oliveira ◽  
Marcelo Santini
Keyword(s):  
Latent Heat ◽  
Large Scale ◽  
Surface Wind ◽  
Heat Fluxes ◽  
Lower Atmosphere ◽  
Meteorological Variables ◽  
Global Ocean ◽  
Study Region ◽  
Cold Core

The Brazil–Malvinas Confluence (BMC) is one of the most dynamical regions of the global ocean. Its variability is dominated by the mesoscale, mainly expressed by the presence of meanders and eddies, which are understood to be local regulators of air-sea interaction processes. The objective of this work is to study the local modulation of air-sea interaction variables by the presence of either a warm (ED1) and a cold core (ED2) eddy, present in the BMC, during September to November 2013. The translation and lifespans of both eddies were determined using satellite-derived sea level anomaly (SLA) data. Time series of satellite-derived surface wind data, as well as these and other meteorological variables, retrieved from ERA5 reanalysis at the eddies’ successive positions in time, allowed us to investigate the temporal modulation of the lower atmosphere by the eddies’ presence along their translation and lifespan. The reanalysis data indicate a mean increase of 78% in sensible and 55% in latent heat fluxes along the warm eddy trajectory in comparison to the surrounding ocean of the study region. Over the cold core eddy, on the other hand, we noticed a mean reduction of 49% and 25% in sensible and latent heat fluxes, respectively, compared to the adjacent ocean. Additionally, a field campaign observed both eddies and the lower atmosphere from ship-borne observations before, during and after crossing both eddies in the study region during October 2013. The presence of the eddies was imprinted on several surface meteorological variables depending on the sea surface temperature (SST) in the eddy cores. In situ oceanographic and meteorological data, together with high frequency micrometeorological data, were also used here to demonstrate that the local, rather than the large scale forcing of the eddies on the atmosphere above, is, as expected, the principal driver of air-sea interaction when transient atmospheric systems are stable (not actively varying) in the study region. We also make use of the in situ data to show the differences (biases) between bulk heat flux estimates (used on atmospheric reanalysis products) and eddy covariance measurements (taken as “sea truth”) of both sensible and latent heat fluxes. The findings demonstrate the importance of short-term changes (minutes to hours) in both the atmosphere and the ocean in contributing to these biases. We conclude by emphasizing the importance of the mesoscale oceanographic structures in the BMC on impacting local air-sea heat fluxes and the marine atmospheric boundary layer stability, especially under large scale, high-pressure atmospheric conditions.

scholarly journals Averaging-Related Biases in Monthly Latent Heat Fluxes

2012 ◽  
Vol 29 (7) ◽  
pp. 974-986 ◽  
Author(s):  
Paul J. Hughes ◽  
Mark A. Bourassa ◽  
Jeremy J. Rolph ◽  
Shawn R. Smith
Keyword(s):  
Latent Heat ◽  
Classical Method ◽  
Surface Flux ◽  
Heat Fluxes ◽  
Western Boundary ◽  
State University ◽  
Boundary Current ◽  
Surface Energy Fluxes ◽  
Temporal Averaging ◽  
The Impact

Abstract Seasonal-to-multidecadal applications that require ocean surface energy fluxes often require accuracies of surface turbulent fluxes to be 5 W m−2 or better. While there is little doubt that uncertainties in the flux algorithms and input data can cause considerable errors, the impact of temporal averaging has been more controversial. The biases resulting from using monthly averaged winds, temperatures, and humidities in the bulk aerodynamic formula (i.e., the so-called classical method) to estimate the monthly mean latent heat fluxes are shown to be substantial and spatially varying in a manner that is consistent with most prior work. These averaging-related biases are linked to nonnegligible submonthly covariances between the wind, temperature, and humidity. To provide additional insight into the averaging-related bias, the methodology behind the third-generation Florida State University monthly mean surface flux product (FSU3) is detailed to highlight additional sources of errors in gridded datasets. The FSU3 latent heat fluxes suffer from this averaging-related bias, which can be as large as 90 W m−2 in western boundary current regions during winter and can exceed 40 W m−2 in synoptically active portions of the tropics. The regional impacts of these biases on the mixed layer temperature tendency are shown to demonstrate that the error resulting from applying the classical method is physically substantial.

scholarly journals Numerical simulation of extreme snow melt observed at the SIGMA-A site, northwest Greenland, during summer 2012

2015 ◽  
Vol 9 (1) ◽  
pp. 495-539
Author(s):  
M. Niwano ◽  
T. Aoki ◽  
S. Matoba ◽  
S. Yamaguchi ◽  
T. Tanikawa ◽  
...  
Keyword(s):  
Heat Flux ◽  
Grain Size ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
In Situ Measurements ◽  
The Arctic ◽  
Snow Surface ◽  
Near Surface ◽  
Surface Snow

Abstract. The surface energy balance (SEB) from 30 June to 14 July 2012 at site SIGMA (Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic)-A, (78°03' N, 67°38' W; 1490 m a.s.l.) on the northwest Greenland Ice Sheet (GrIS) was investigated by using in situ atmospheric and snow measurements, as well as numerical modeling with a one-dimensional, multi-layered, physical snowpack model called SMAP (Snow Metamorphism and Albedo Process). At SIGMA-A, remarkable near-surface snowmelt and continuous heavy rainfall (accumulated precipitation between 10 and 14 July was estimated to be 100 mm) were observed after 10 July 2012. Application of the SMAP model to the GrIS snowpack was evaluated based on the snow temperature profile, snow surface temperature, surface snow grain size, and shortwave albedo, all of which the model simulated reasonably well. However, comparison of the SMAP-calculated surface snow grain size with in situ measurements during the period when surface hoar with small grain size was observed on-site revealed that it was necessary to input air temperature, relative humidity, and wind speed data from two heights to simulate the latent heat flux into the snow surface and subsequent surface hoar formation. The calculated latent heat flux was always directed away from the surface if data from only one height were input to the SMAP model, even if the value for roughness length of momentum was perturbed between the possible maximum and minimum values in numerical sensitivity tests. This result highlights the need to use two-level atmospheric profiles to obtain realistic latent heat flux. Using such profiles, we calculated the SEB at SIGMA-A from 30 June to 14 July 2012. Radiation-related fluxes were obtained from in situ measurements, whereas other fluxes were calculated with the SMAP model. By examining the components of the SEB, we determined that low-level clouds accompanied by a significant temperature increase played an important role in the melt event observed at SIGMA-A. These conditions induced a remarkable surface heating via cloud radiative forcing in the polar region.

The limitations of in situ data for validating satellite-derived spatio-temporal ocean products.

2021 ◽  
Author(s):  
Rory Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack
Keyword(s):  
Surface Temperature ◽  
Surface Heterogeneity ◽  
Ocean Surface ◽  
Bias Reduction ◽  
Data Validation ◽  
Near Surface ◽  
In Situ Data ◽  
Spatio Temporal ◽  
Image Pixels

<p>The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.</p><p>For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.</p><p>A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.</p><p>Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman’s Rank, n=14, ρ[12]= +0.5755, P<0.05), and kurtosis (Spearman’s Rank, n=14, ρ[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.</p>

scholarly journals The Role of Latent Heat Flux in Tropical Cyclogenesis over the Western North Pacific: Comparison of Developing versus Non-Developing Disturbances

2019 ◽  
Vol 7 (2) ◽  
pp. 28 ◽  
Author(s):  
Si Gao ◽  
Shengbin Jia ◽  
Yanyu Wan ◽  
Tim Li ◽  
Shunan Zhai ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
North Pacific ◽  
Western North Pacific ◽  
Specific Humidity ◽  
Near Surface ◽  
Tangential Wind

The possible role of air–sea latent heat flux (LHF) in tropical cyclone (TC) genesis over the western North Pacific (WNP) is investigated using state-of-the-art satellite and analysis datasets. The authors conducted composite analyses of several meteorological variables after identifying developing and non-developing tropical disturbances from June to October of the period 2000 to 2009. Compared to the non-developing disturbances, increased LHF underlying the developing disturbances enhances boundary–layer specific humidity. The secondary circulation then transports more boundary–layer moisture inward and upward and, thus, induces a stronger moist core in the middle troposphere. Accordingly, the air in the core region ascends following a warmer moist adiabat than that in the environment and results in a stronger upper-level warm core, which is associated with a stronger near-surface tangential wind based on the thermal wind balance. This enlarges the magnitude and negative radial gradient of LHF and, thereby, further increases boundary–layer specific humidity. A tropical depression forms when the near-surface tangential wind increases to a certain extent as a result of the continuing positive feedback between near-surface wind and LHF. The results suggest an important role of wind-driven LHF in TC genesis over the WNP.

scholarly journals On the Assessment of a Cooling Tower Scheme for High-Resolution Numerical Weather Modeling for Urban Areas

2019 ◽  
Vol 58 (6) ◽  
pp. 1399-1415 ◽  
Author(s):  
Miao Yu ◽  
Jorge González ◽  
Shiguang Miao ◽  
Prathap Ramamurthy
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Urban Areas ◽  
Cooling Tower ◽  
Absolute Humidity ◽  
Heat Fluxes ◽  
Anthropogenic Heat ◽  
Near Surface ◽  
Anthropogenic Heat Flux ◽  
Temperature Errors

AbstractA cooling tower scheme that quantifies the sensible and latent anthropogenic heat fluxes released from buildings was coupled to an operational forecasting system [Rapid Refresh Multiscale Analysis and Prediction of the Beijing Urban Meteorological Institute (RMAPS-Urban)] and was evaluated in the context of the megacity of Beijing, China, during summer months. The objective of this scheme is to correct for underestimations of surface latent heat fluxes in regional climate modeling and weather forecasts in urban areas. The performance for surface heat fluxes by the modified RMAPS-Urban is greatly improved when compared with a suite of observations in Beijing. The cooling tower scheme increases the anthropogenic latent heat partition by 90% of the total anthropogenic heat flux release. Averaged surface latent heat flux in urban areas increases to about 64.3 W m−2 with a peak of 150 W m−2 on dry summer days and 40.35 W m−2 with a peak of 150 W m−2 on wet summer days. The model performance of near-surface temperature and humidity is also improved. Average 2-m temperature errors are reduced by 1°C, and maximum and minimum temperature errors are improved by 2°–3°C; absolute humidity is increased by 5%.

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