Contrasting the co-variability of daytime cloud and precipitation over tropical land and ocean

Abstract. The co-variability of cloud and precipitation in the extended tropics (35∘ N–35∘ S) is investigated using contemporaneous data sets for a 13-year period. The goal is to quantify potential relationships between cloud type fractions and precipitation events of particular strength. Particular attention is paid to whether the relationships exhibit different characteristics over tropical land and ocean. A primary analysis metric is the correlation coefficient between fractions of individual cloud types and frequencies within precipitation histogram bins that have been matched in time and space. The cloud type fractions are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) joint histograms of cloud top pressure and cloud optical thickness in 1∘ grid cells, and the precipitation frequencies come from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) data set aggregated to the same grid. It is found that the strongest coupling (positive correlation) between clouds and precipitation occurs over ocean for cumulonimbus clouds and the heaviest rainfall. While the same cloud type and rainfall bin are also best correlated over land compared to other combinations, the correlation magnitude is weaker than over ocean. The difference is attributed to the greater size of convective systems over ocean. It is also found that both over ocean and land the anti-correlation of strong precipitation with “weak” (i.e., thin and/or low) cloud types is of greater absolute strength than positive correlations between weak cloud types and weak precipitation. Cloud type co-occurrence relationships explain some of the cloud–precipitation anti-correlations. Weak correlations between weaker rainfall and clouds indicate poor predictability for precipitation when cloud types are known, and this is even more true over land than over ocean.

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Contrasting the Co-variability of Daytime Cloud and Precipitation over Tropical Land and Ocean

10.5194/acp-2017-612 ◽

2017 ◽

Author(s):

Daeho Jin ◽

Lazaros Oreopoulos ◽

Dongmin Lee ◽

Nayeong Cho ◽

Jackson Tan

Keyword(s):

Correlation Coefficients ◽

Tropical Rainfall Measuring Mission ◽

Cloud Type ◽

Convective Systems ◽

Low Cloud ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Difference ◽

Histogram Bins ◽

Positive Correlations ◽

The Relationship

Abstract. The co-variability of cloud and precipitation in the extended tropics (35° N–35° S) is investigated using contemporaneous datasets for a 13-year period. The goal is to quantify the relationship between cloud types and precipitation events of particular strength. Particular attention is paid to whether the relationship exhibits different characteristics over tropical land and ocean. A major analysis metric is correlation coefficients between fractions of individual cloud types and frequencies within precipitation histogram bins that have been matched in time and space. The cloud type fractions are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) joint histograms of cloud top pressure and cloud optical thickness in one-degree grid cells, and the precipitation frequencies come from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) dataset aggregated to the same grid. It is found that the strongest coupling (positive correlation) between clouds and precipitation occurs for cumulonimbus clouds and heaviest rainfall over ocean. While the same cloud type and rainfall bin are also best correlated over land compared to other combinations, the correlation magnitude over land is weaker than over ocean. The difference is attributed to the greater size of convective systems over ocean. It is also found both over ocean and land that the anti-correlation of strong precipitation with weak (i.e., thin and/or low) cloud types is of greater absolute strength than positive correlations between weak cloud types and weak precipitation. Cloud type co-occurrence relationships explain some of the cloud-precipitation anti-correlations. Couplings between weaker rainfall and clouds are also distinct in ocean vs. land, with precipitation predictability when cloud type is known being quite poor in general, particularly over land.

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The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

Atmospheric Measurement Techniques ◽

10.5194/amt-8-421-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 421-434 ◽

Cited By ~ 18

Author(s):

M. P. Jensen ◽

T. Toto ◽

D. Troyan ◽

P. E. Ciesielski ◽

D. Holdridge ◽

...

Keyword(s):

Large Scale ◽

Scale Model ◽

Data Sets ◽

Central Plains ◽

Data Set ◽

Convective Systems ◽

Convective Clouds ◽

Quality Checks ◽

Network Operations ◽

The Impact

Abstract. The Midlatitude Continental Convective Clouds Experiment (MC3E) took place during the spring of 2011 centered in north-central Oklahoma, USA. The main goal of this field campaign was to capture the dynamical and microphysical characteristics of precipitating convective systems in the US Central Plains. A major component of the campaign was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state with the intent of deriving model forcing data sets. Over the course of the 46-day MC3E campaign, a total of 1362 radiosondes were launched from the enhanced sonde network. This manuscript provides details on the instrumentation used as part of the sounding array, the data processing activities including quality checks and humidity bias corrections and an analysis of the impacts of bias correction and algorithm assumptions on the determination of convective levels and indices. It is found that corrections for known radiosonde humidity biases and assumptions regarding the characteristics of the surface convective parcel result in significant differences in the derived values of convective levels and indices in many soundings. In addition, the impact of including the humidity corrections and quality controls on the thermodynamic profiles that are used in the derivation of a large-scale model forcing data set are investigated. The results show a significant impact on the derived large-scale vertical velocity field illustrating the importance of addressing these humidity biases.

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Evaluation of the Simard et al. 2011 Global Canopy Height Map in Boreal Forests

Remote Sensing ◽

10.3390/rs12071114 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1114

Author(s):

Wei Yang ◽

Akihiko Kondoh

Keyword(s):

Boreal Forests ◽

Forest Canopy ◽

Spatial Separation ◽

Canopy Height ◽

Tree Cover ◽

Data Sets ◽

Data Set ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Light detection and ranging (LiDAR) provides a state-of-the-art technique for measuring forest canopy height. Nevertheless, it may miss some forests due to its spatial separation of individual spots. A number of efforts have been made to overcome the limitation of global LiDAR datasets to generate wall-to-wall canopy height products, among which a global satellite product produced by Simard et al. (2011) (henceforth, the Simard-map) has been the most widely applied. However, the accuracy of the Simard-map is uncertain in boreal forests, which play important roles in the terrestrial carbon cycle and are encountering more extensive climate changes than the global average. In this letter, we evaluated the Simard-map in boreal forests through a literature review of field canopy height. Our comparison shows that the Simard-map yielded a significant correlation with the field canopy height (R2 = 0.68 and p < 0.001). However, remarkable biases were observed with the root mean square error (RMSE), regression slope, and intercept of 6.88 m, 0.448, and 10.429, respectively. Interestingly, we found that the evaluation results showed an identical trend with a validation of moderate-resolution imaging spectroradiometer (MODIS) tree-cover product (MOD44B) in boreal forests, which was used as a crucial input data set for generating the Simard-map. That is, both the Simard-map and MOD44B yielded an overestimation (underestimation) in the lower (upper) tails of the scatterplots between the field and satellite data sets. This indicates that the MOD44B product is the likely source of error for the estimation biases of the Simard-map. Finally, a field calibration was performed to improve the Simard-map in boreal forests by compensating for the estimation biases and discarding non-forest areas, which provided a more reliable canopy height product for future applications.

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Precipitation Trends over the Indus Basin

Climate ◽

10.3390/cli7100116 ◽

2019 ◽

Vol 7 (10) ◽

pp. 116 ◽

Cited By ~ 5

Author(s):

Nir Y. Krakauer ◽

Tarendra Lakhankar ◽

Ghulam H. Dars

Keyword(s):

Remote Sensing ◽

Large Population ◽

Tropical Rainfall Measuring Mission ◽

Indus Basin ◽

Data Sets ◽

Precipitation Trend ◽

Precipitation Data ◽

Data Set ◽

Station Data ◽

Precipitation Trends

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in such cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

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A Ccomparison of sedigraph and pipette methods for soil particle-size analysis

Soil Research ◽

10.1071/sr9930407 ◽

1993 ◽

Vol 31 (4) ◽

pp. 407 ◽

Cited By ~ 22

Author(s):

GD Buchan ◽

KS Grewal ◽

JJ Claydon ◽

RJ Mcpherson

Keyword(s):

Particle Size ◽

New Zealand ◽

Particle Size Analysis ◽

Size Analysis ◽

Data Sets ◽

Regression Equations ◽

Total Iron Content ◽

Data Set ◽

X Ray ◽

The Difference

The X-ray attenuation (Sedigraph) method for particle-size analysis is known to consistently estimate a finer size distribution than the pipette method. The objectives of this study were to compare the two methods, and to explore the reasons for their divergence. The methods are compared using two data sets from measurements made independently in two New Zealand laboratories, on two different sets of New Zealand soils, covering a range of textures and parent materials. The Sedigraph method gave systematically greater mass percentages at the four measurement diameters (20, 10, 5 and 2 �m). For one data set, the difference between clay (<2 �m) percentages from the two methods is shown to be positively correlated (R2 = 0.625) with total iron content of the sample, for all but one of the soils. This supports a novel hypothesis that the typically greater concentration of Fe (a strong X-ray absorber) in smaller size fractions is the major factor causing the difference. Regression equations are presented for converting the Sedigraph data to their pipette equivalents.

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Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-1573-2018 ◽

2018 ◽

Vol 18 (3) ◽

pp. 1573-1592 ◽

Cited By ~ 48

Author(s):

Gerrit de Leeuw ◽

Larisa Sogacheva ◽

Edith Rodriguez ◽

Konstantinos Kourtidis ◽

Aristeidis K. Georgoulias ◽

...

Keyword(s):

Time Series ◽

European Space Agency ◽

Mainland China ◽

Satellite Observations ◽

Data Sets ◽

Data Set ◽

Modis Aod ◽

The Difference ◽

Along Track ◽

Aerosol Properties

Abstract. The retrieval of aerosol properties from satellite observations provides their spatial distribution over a wide area in cloud-free conditions. As such, they complement ground-based measurements by providing information over sparsely instrumented areas, albeit that significant differences may exist in both the type of information obtained and the temporal information from satellite and ground-based observations. In this paper, information from different types of satellite-based instruments is used to provide a 3-D climatology of aerosol properties over mainland China, i.e., vertical profiles of extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a lidar flying aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the column-integrated extinction (aerosol optical depth – AOD) available from three radiometers: the European Space Agency (ESA)'s Along-Track Scanning Radiometer version 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR) (together referred to as ATSR) and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, together spanning the period 1995–2015. AOD data are retrieved from ATSR using the ATSR dual view (ADV) v2.31 algorithm, while for MODIS Collection 6 (C6) the AOD data set is used that was obtained from merging the AODs obtained from the dark target (DT) and deep blue (DB) algorithms, further referred to as the DTDB merged AOD product. These data sets are validated and differences are compared using Aerosol Robotic Network (AERONET) version 2 L2.0 AOD data as reference. The results show that, over China, ATSR slightly underestimates the AOD and MODIS slightly overestimates the AOD. Consequently, ATSR AOD is overall lower than that from MODIS, and the difference increases with increasing AOD. The comparison also shows that neither of the ATSR and MODIS AOD data sets is better than the other one everywhere. However, ATSR ADV has limitations over bright surfaces which the MODIS DB was designed for. To allow for comparison of MODIS C6 results with previous analyses where MODIS Collection 5.1 (C5.1) data were used, also the difference between the C6 and C5.1 merged DTDB data sets from MODIS/Terra over China is briefly discussed. The AOD data sets show strong seasonal differences and the seasonal features vary with latitude and longitude across China. Two-decadal AOD time series, averaged over all of mainland China, are presented and briefly discussed. Using the 17 years of ATSR data as the basis and MODIS/Terra to follow the temporal evolution in recent years when the environmental satellite Envisat was lost requires a comparison of the data sets for the overlapping period to show their complementarity. ATSR precedes the MODIS time series between 1995 and 2000 and shows a distinct increase in the AOD over this period. The two data series show similar variations during the overlapping period between 2000 and 2011, with minima and maxima in the same years. MODIS extends this time series beyond the end of the Envisat period in 2012, showing decreasing AOD.

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Evaluative application of UKCP09‐based downscaled future weather years to simulate overheating risk in typical English homes

Structural Survey ◽

10.1108/ss-01-2013-0005 ◽

2013 ◽

Vol 31 (4) ◽

pp. 231-252 ◽

Cited By ~ 1

Author(s):

Rajat Gupta ◽

Matthew Gregg ◽

Hu Du ◽

Katie Williams

Keyword(s):

Climate Change ◽

Simulation Modelling ◽

Simulation Software ◽

Building Simulation ◽

Future Research ◽

Climate Projections ◽

Data Sets ◽

Data Set ◽

Content Type ◽

The Difference

PurposeTo critically compare three future weather year (FWY) downscaling approaches, based on the 2009 UK Climate Projections, used for climate change impact and adaptation analysis in building simulation software.Design/methodology/approachThe validity of these FWYs is assessed through dynamic building simulation modelling to project future overheating risk in typical English homes in 2050s and 2080s.FindingsThe modelling results show that the variation in overheating projections is far too significant to consider the tested FWY data sets equally suitable for the task.Research and practical implicationsIt is recommended that future research should consider harmonisation of the downscaling approaches so as to generate a unified data set of FWYs to be used for a given location and climate projection. If FWY are to be used in practice, live projects will need viable and reliable FWY on which to base their adaptation decisions. The difference between the data sets tested could potentially lead to different adaptation priorities specifically with regard to time series and adaptation phasing through the life of a building.Originality/valueThe paper investigates the different results derived from FWY application to building simulation. The outcome and implications are important considerations for research and practice involved in FWY data use in building simulation intended for climate change adaptation modelling.

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SCIAMACHY WFM-DOAS XCO<sub>2</sub>: comparison with CarbonTracker XCO<sub>2</sub> focusing on aerosols and thin clouds

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-5-2887-2012 ◽

2012 ◽

Vol 5 (2) ◽

pp. 2887-2931 ◽

Cited By ~ 2

Author(s):

J. Heymann ◽

O. Schneising ◽

M. Reuter ◽

M. Buchwitz ◽

V. V. Rozanov ◽

...

Keyword(s):

Southern Hemisphere ◽

Temporal Correlation ◽

Anthropogenic Activity ◽

Data Sets ◽

Spatial Correlations ◽

Data Set ◽

Cloud Data ◽

Temporal Correlations ◽

Retrieval Errors ◽

The Difference

Abstract. Carbon dioxide (CO2) is the most important greenhouse gas whose atmospheric loading has been significantly increased by anthropogenic activity leading to global warming. Accurate measurements and models are needed in order to reliably predict our future climate. This, however, has challenging requirements. Errors in measurements and models need to be identified and minimised. In this context, we present a comparison between satellite-derived column-averaged dry air mole fractions of CO2, denoted XCO2, retrieved from SCIAMACHY/ENVISAT using the WFM-DOAS algorithm, and output from NOAA's global CO2 modelling and assimilation system CarbonTracker. We investigate to what extent differences between these two data sets are influenced by systematic retrieval errors due to aerosols and unaccounted clouds. We analyse seven years of SCIAMACHY WFM-DOAS version 2.1 retrievals (WFMDv2.1) using the latest version of CarbonTracker (version 2010). We investigate to what extent the difference between SCIAMACHY and CarbonTracker XCO2 are temporally and spatially correlated with global aerosol and cloud data sets. For this purpose, we use a global aerosol data set generated within the European GEMS project, which is based on assimilated MODIS satellite data. For clouds, we use a data set derived from CALIOP/CALIPSO. We find significant correlations of the SCIAMACHY minus CarbonTracker XCO2 difference with thin clouds over the Southern Hemisphere. The maximum temporal correlation we find for Darwin, Australia (r2 = 54%). Large temporal correlations with thin clouds are also observed over other regions of the Southern Hemisphere (e.g. 43% for South America and 31% for South Africa). Over the Northern Hemisphere the temporal correlations are typically much lower. An exception is India, where large temporal correlations with clouds and aerosols have also been found. For all other regions the temporal correlations with aerosol are typically low. For the spatial correlations the picture is less clear. They are typically low for both aerosols and clouds, but dependent on region and season, they may exceed 30% (the maximum value of 46% has been found for Darwin during September to November). Overall we find that the presence of thin clouds can potentially explain a significant fraction of the difference between SCIAMACHY WFMDv2.1 XCO2 and CarbonTracker over the Southern Hemisphere. Aerosols appear to be less of a problem. Our study indicates that the quality of the satellite derived XCO2 will significantly benefit from a reduction of scattering related retrieval errors at least for the Southern Hemisphere.

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The SPARC water vapour assessment II: Comparison of stratospheric and lower mesospheric water vapour time series observed from satellites

10.5194/amt-2018-33 ◽

2018 ◽

Author(s):

Farahnaz Khosrawi ◽

Stefan Lossow ◽

Gabriele P. Stiller ◽

Karen H. Rosenlof ◽

Joachim Urban ◽

...

Keyword(s):

Time Series ◽

Water Vapour ◽

Data Sets ◽

Data Set ◽

The Future ◽

Modelling Studies ◽

The Difference ◽

The Tropics ◽

The Antarctic ◽

Satellite Instruments

Abstract. Time series of stratospheric and lower mesospheric water vapour using 33 data sets from 15 different satellite instruments were compared in the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II). This comparison aimed to provide a comprehensive overview of the typical uncertainties in the observational database that can be considered in the future in observational and modelling studies addressing e.g stratospheric water vapour trends. The time series comparisons are presented for the three latitude bands, the Antarctic (80°–70° S), the tropics (15° S–15° N) and the northern hemisphere mid-latitudes (50° N–60° N) at four different altitudes (0.1, 3, 10 and 80 hPa) covering the stratosphere and lower mesosphere. The combined temporal coverage of observations from the 15 satellite instruments allowed considering the time period 1986–2014. In addition to the qualitative comparison of the time series, the agreement of the data sets is assessed quantitatively in the form of the spread (i.e. the difference between the maximum and minimum volume mixing ratio among the data sets), the (Pearson) correlation coefficient and the drift (i.e. linear changes of the difference between time series over time). Generally, good agreement between the time series was found in the middle stratosphere while larger differences were found in the lower mesosphere and near the tropopause. Concerning the latitude bands, the largest differences were found in the Antarctic while the best agreement was found for the tropics. From our assessment we find that all data sets can be considered in the future in observational and modelling studies addressing e.g. stratospheric and lower mesospheric water vapour variability and trends when data set specific characteristics (e.g. a drift) and restrictions (e.g. temporal and spatial coverage) are taken into account.

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Comparison of 14C Ages Between LSC and AMS Measurements of Choukai Jindai Cedar Tree Rings at 2600 cal BP

Radiocarbon ◽

10.1017/s0033822200045987 ◽

2010 ◽

Vol 52 (3) ◽

pp. 895-900 ◽

Cited By ~ 5

Author(s):

Yui Takahashi ◽

Hirohisa Sakurai ◽

Kayo Suzuki ◽

Taiichi Sato ◽

Shuichi Gunji ◽

...

Keyword(s):

Mass Spectrometry ◽

Tree Rings ◽

Liquid Scintillation ◽

Measurement Methods ◽

Scintillation Counting ◽

Age Difference ◽

Data Sets ◽

Data Set ◽

The Difference ◽

Good Agreement

Radiocarbon ages of Choukai Jindai cedar tree rings growing in the excess era of 14C concentrations during 2757–2437 cal BP were measured using 2 types of 14C measurement methods, i.e. liquid scintillation counting (LSC) and accelerator mass spectrometry (AMS). The difference between the 2 methods is 3.7 ± 5.2 14C yr on average for 61 single-year tree rings, indicating good agreement between the methods. The Choukai data sets show a small sharp bump with an average 14C age of 2497.1 ± 3.0 14C yr BP during 2650–2600 cal BP. Although the profile of the Choukai LSC data set compares well with that of IntCal04, having a 14C age difference of 4.6 ± 5.3 14C yr on average, the Choukai LSC 14C ages indicate variability against the smoothed profile of IntCal04.

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