Evaluating the Accuracy of a Gridded Near-Surface Temperature Dataset over Mainland China

High-resolution meteorological data products are crucial for agrometeorological studies. Here, we study the accuracy of an important gridded dataset, the near-surface temperature dataset from the 5 km × 5 km resolution China dataset of meteorological forcing for land surface modeling (published by the Beijing Normal University). Using both the gridded dataset and the observed temperature data from 590 meteorological stations, we calculate nine universal meteorological indices (mean, maximum, and minimum temperatures of daily, monthly, and annual data) and five agricultural thermal indices (first frost day, last frost day, frost-free period, and ≥0 °C and ≥10 °C active accumulated temperature, i.e., AAT0 and AAT10) of the 11 temperature zones over mainland China. Then, for each meteorological index, we calculate the root mean square errors (RMSEs), correlation coefficient and climate trend rates of the two datasets. The results show that the RMSEs of these indices are usually lower in the north subtropical, mid-subtropical, south subtropical, marginal tropical and mid-tropical zones than in the plateau subfrigid, plateau temperate, and plateau subtropical mountains zones. Over mainland China, the AAT0, AAT10, and mean and maximum temperatures calculated from the gridded data show the same climate trends with those derived from the observed data, while the minimum temperature and its derivations (first frost day, last frost day, and frost-free period) show the opposite trends in many areas. Thus, the mean and maximum temperature data derived from the gridded dataset are applicable for studies in most parts of China, but caution should be taken when using the minimum temperature data.

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Trends in Mean Annual Minimum and Maximum Near Surface Temperature in Nairobi City, Kenya

Advances in Meteorology ◽

10.1155/2010/676041 ◽

2010 ◽

Vol 2010 ◽

pp. 1-6 ◽

Cited By ~ 9

Author(s):

George Lukoye Makokha ◽

Chris A. Shisanya

Keyword(s):

Urban Planning ◽

Surface Temperature ◽

Minimum Temperature ◽

Central Business District ◽

Maximum Temperature ◽

Rank Test ◽

Homogeneity Test ◽

Near Surface ◽

Business District ◽

The City

This paper examines the long-term urban modification of mean annual conditions of near surface temperature in Nairobi City. Data from four weather stations situated in Nairobi were collected from the Kenya Meteorological Department for the period from 1966 to 1999 inclusive. The data included mean annual maximum and minimum temperatures, and was first subjected to homogeneity test before analysis. Both linear regression and Mann-Kendall rank test were used to discern the mean annual trends. Results show that the change of temperature over the thirty-four years study period is higher for minimum temperature than maximum temperature. The warming trends began earlier and are more significant at the urban stations than is the case at the sub-urban stations, an indication of the spread of urbanisation from the built-up Central Business District (CBD) to the suburbs. The established significant warming trends in minimum temperature, which are likely to reach higher proportions in future, pose serious challenges on climate and urban planning of the city. In particular the effect of increased minimum temperature on human physiological comfort, building and urban design, wind circulation and air pollution needs to be incorporated in future urban planning programmes of the city.

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A new approach for modeling near surface temperature lapse rate based on normalized land surface temperature data

Remote Sensing of Environment ◽

10.1016/j.rse.2020.111746 ◽

2020 ◽

Vol 242 ◽

pp. 111746 ◽

Cited By ~ 1

Author(s):

Mohammad Karimi Firozjaei ◽

Solmaz Fathololoumi ◽

Seyed Kazem Alavipanah ◽

Majid Kiavarz ◽

Ali Reza Vaezi ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Temperature Data ◽

Lapse Rate ◽

Near Surface ◽

New Approach ◽

Temperature Lapse Rate ◽

Surface Temperature Data

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Effects of Historical Urbanization in the Brussels Capital Region on Surface Air Temperature Time Series: A Model Study

Journal of Applied Meteorology and Climatology ◽

10.1175/2009jamc2140.1 ◽

2009 ◽

Vol 48 (10) ◽

pp. 2181-2196 ◽

Cited By ~ 23

Author(s):

R. Hamdi ◽

A. Deckmyn ◽

P. Termonia ◽

G. R. Demarée ◽

P. Baguis ◽

...

Keyword(s):

Land Surface ◽

Minimum Temperature ◽

Computational Cost ◽

Land Surface Model ◽

Maximum Temperature ◽

Surface Model ◽

Impervious Surfaces ◽

Urban Bias ◽

Near Surface ◽

Capital Region

Abstract The authors examine the local impact of change in impervious surfaces in the Brussels capital region (BCR), Belgium, on trends in maximum, minimum, and mean temperatures between 1960 and 1999. Specifically, data are combined from remote sensing imagery and a land surface model including state-of-the-art urban parameterization—the Town Energy Balance scheme. To (i) isolate effects of urban growth on near-surface temperature independent of atmospheric circulations and (ii) be able to run the model over a very long period without any computational cost restrictions, the land surface model is run in a stand-alone mode coupled to downscaled 40-yr ECMWF reanalysis data. BCR was considered a lumped urban volume and the rate of urbanization was assessed by estimating the percentage of impervious surfaces from Landsat images acquired for various years. Model simulations show that (i) the annual mean urban bias (AMUB) on minimum temperature is rising at a higher rate (almost 3 times more) than on maximum temperature, with a linear trend of 0.14° and 0.05°C (10 yr)−1, respectively, (ii) the 40-yr AMUB on mean temperature is estimated to be 0.62°C, (iii) 45% of the overall warming trend is attributed to intensifying urban heat island effects rather than to changes in local–regional climate, and (iv) during summertime, a stronger dependence between the increase of urban bias on minimum temperature and the change in percentage of impervious surfaces is found.

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Prediction of solar radiation with air temperature data in a coastal location in Tamilnadu

MAUSAM ◽

10.54302/mausam.v62i1.208 ◽

2021 ◽

Vol 62 (1) ◽

pp. 85-90

Author(s):

A. MUGRAPAN ◽

SUBBARAYAN SIVAPRAKASAN ◽

S. MOHAN

Keyword(s):

Solar Radiation ◽

Air Temperature ◽

Minimum Temperature ◽

Reference Evapotranspiration ◽

Temperature Data ◽

Maximum Temperature ◽

Adjustment Coefficient ◽

Meteorological Observatory ◽

Daily Sunshine ◽

Sunshine Hour

The objective of this study is to evaluate the performance of the Hargreaves’ Radiation formula in estimating daily solar radiation for an Indian coastal location namely Annamalainagar in Tamilnadu State. Daily solar radiation by Hargreaves’ Radiation formula was computed using the observed data of maximum temperature, Tmax and minimum temperature, Tmin, sourced from the India Meteorological Observatory located at Annamalainagar and employing the adjustment coefficient KRS of 0.19. Daily solar radiation was also computed using Angstrom-Prescott formula with the measured daily sunshine hour data. The differences between the daily solar radiation values computed using the formulae were more pronounced in year around. Hence, the adjustment coefficient KRS is calibrated for the study location under consideration so that the calibrated KRS could be used to better predict daily solar radiation and hence better estimation of reference evapotranspiration.

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Thermal Signatures of Peri-Urban Landscapes

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0292.1 ◽

2020 ◽

Vol 59 (9) ◽

pp. 1443-1452 ◽

Cited By ~ 1

Author(s):

William A. Gough

Keyword(s):

Surface Temperature ◽

Minimum Temperature ◽

Maximum Temperature ◽

Urban Landscapes ◽

Cold Surface ◽

Historical Climate ◽

Mean Temperature ◽

Climate Records ◽

Urban Rural ◽

Rural Sites

AbstractA new thermal metric is examined that is based on the ratio of day-to-day warm and cold surface temperature transitions. Urban and rural sites in Canada are examined using this new metric for the minimum temperature, maximum temperature, and mean temperature of the day. A distinctive signature emerges for “peri-urban” landscapes—landscapes at the urban–rural interface—and thus may provide a useful and relatively easy way to detect such environments using the current and historical climate records. A climatological basis for the presence of these distinct thermal signatures in peri-urban landscapes is proposed.

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Impacts of Irrigation on Summertime Temperatures in the Pacific Northwest

Earth Interactions ◽

10.1175/ei-d-19-0015.1 ◽

2020 ◽

Vol 24 (1) ◽

pp. 1-26

Author(s):

Patricia M. Lawston ◽

Joseph A. Santanello ◽

Brian Hanson ◽

Kristi Arsensault

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Temperature ◽

Land Surface ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Climate Signal ◽

Near Surface ◽

Surface Temperatures ◽

Land Surface Temperatures

AbstractIrrigation has the potential to modify local weather and regional climate through a repartitioning of water among the surface, soil, and atmosphere with the potential to drastically change the terrestrial energy budget in agricultural areas. This study uses local observations, satellite remote sensing, and numerical modeling to 1) explore whether irrigation has historically impacted summer maximum temperatures in the Columbia Plateau, 2) characterize the current extent of irrigation impacts to soil moisture (SM) and land surface temperature (LST), and 3) better understand the downstream extent of irrigation’s influence on near-surface temperature, humidity, and boundary layer development. Analysis of historical daily maximum temperature (TMAX) observations showed that the three Global Historical Climate Network (GHCN) sites downwind of Columbia Basin Project (CBP) irrigation experienced statistically significant cooling of the mean summer TMAX by 0.8°–1.6°C in the post-CBP (1968–98) as compared to pre-CBP expansion (1908–38) period, opposite the background climate signal. Remote sensing observations of soil moisture and land surface temperatures in more recent years show wetter soil (~18%–25%) and cooler land surface temperatures over the irrigated areas. Simulations using NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) Model support the historical analysis, confirming that under the most common summer wind flow regime, irrigation cooling can extend as far downwind as the locations of these stations. Taken together, these results suggest that irrigation expansion may have contributed to a reduction in summertime temperatures and heat extremes within and downwind of the CBP area. This supports a regional impact of irrigation across the study area.

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Comparison of Near-Surface Air Temperatures and MODIS Ice-Surface Temperatures at Summit, Greenland (2008–13)

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-14-0023.1 ◽

2014 ◽

Vol 53 (9) ◽

pp. 2171-2180 ◽

Cited By ~ 24

Author(s):

Christopher A. Shuman ◽

Dorothy K. Hall ◽

Nicolo E. DiGirolamo ◽

Thomas K. Mefford ◽

Michael J. Schnaubelt

Keyword(s):

Surface Temperature ◽

Temperature Data ◽

Quality Data ◽

High Temporal Resolution ◽

Near Surface ◽

Annual Cycles ◽

In Situ Data ◽

Ice Surface ◽

Surface Temperatures

AbstractThe stability of the Moderate Resolution Imaging Spectroradiometer (MODIS) ice-surface temperature (IST) product from Terra was investigated for use as a climate-quality data record. The availability of climate-quality air temperature data TA from a NOAA observatory at Greenland’s Summit Station has enabled this high-temporal-resolution study of MODIS ISTs. During a >5-yr period (July 2008–August 2013), more than 2500 IST values were compared with ±3-min-average TA values from NOAA’s primary 2-m temperature sensor. This enabled an expected small offset between air and ice-sheet surface temperatures (TA > IST) to be investigated over multiple annual cycles. The principal findings of this study show 1) that IST values are slightly colder than the TA values near freezing but that this offset increases as temperature decreases and 2) that there is a pattern in IST–TA differences as the solar zenith angle (SoZA) varies annually. This latter result largely explains the progressive offset from the in situ data at colder temperatures but also indicates that the MODIS cloud mask is less accurate approaching and during the polar night. The consistency of the results over each year in this study indicates that MODIS provides a platform for remotely deriving surface temperature data, with the resulting IST data being most compatible with in situ TA data when the sky is clear and the SoZA is less than ~85°. The ongoing development of the IST dataset should benefit from improved cloud filtering as well as algorithm modifications to account for the progressive offset from TA at colder temperatures.

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A Statistical Model for Predicting Neutropenic Fever

Blood ◽

10.1182/blood.v124.21.5258.5258 ◽

2014 ◽

Vol 124 (21) ◽

pp. 5258-5258

Author(s):

Ariel Nelson ◽

Dan Eastwood ◽

Tao Wang ◽

Karen Carlson ◽

Laura C Michaelis ◽

...

Keyword(s):

Logistic Regression ◽

Febrile Neutropenia ◽

Predictive Value ◽

Minimum Temperature ◽

Temperature Data ◽

Temperature Series ◽

Series Data ◽

Maximum Temperature ◽

Current Definition ◽

Single Temperature

Abstract Background Febrile neutropenia (FN) is a common occurrence associated with chemotherapy regimens used in patients (pts) with acute myelogenous leukemia (AML). Febrile neutropenia is presently defined as a single temperature of ≥38.3°C (101°F) or a temperature of ≥38.0°C (100.4°F) for >1 hour in a patient with an absolute neutrophil count < 500/mm3. Due to the potential for life threatening infections, fever in a patient with neutropenia is considered an oncologic emergency. Initiating appropriate antibiotic therapy as soon as possible in these patients leads to better outcomes. However, to our knowledge, there is no evidence that supports the current definition of neutropenic fever. Aim To identify a temperature pattern that is predictive of the subsequent development of febrile neutropenia in neutropenic pts with AML Methods After obtaining IRB approval we retrospectively obtained demographic and temperature data from hospitalized patients with AML undergoing induction therapy who were admitted to our institution between 12/8/2012 and 12/7/2013. Temperature data was recorded at intervals per physician order and nursing discretion during admission. We identified fever as a single temperature ≥38.3°C (101°F) or consecutive temperatures recorded 1 hour apart ≥38.06°C (100.5°F). Data was processed using SAS data programming to create and summarize this pilot temperature series data. Data for 68 patients containing 137 fever events was divided into 203 segments: a series was considered to end at the time of fever (or end of data) and a new series for the same patient began 24 hours after a preceding fever. Plots were created showing temperature over time leading up to fever (end of series). Our data consists of unequal interval time series data and does not lend itself to the usual methods of statistical ROC analysis. An ROC-like analysis to estimate sensitivity and specificity of a maximum temperature that predicts for a subsequent episode of FN was performed. Temperature data was subset into 7 time intervals: a pre-fever interval spanning 4 to 28 hours preceding fever or series end, and 6 non-fever intervals, each 24 hours long and spanning the period from 48 to 192 hours before fever or series end, for a total of 854 data windows. Statistics on each patient series within each interval were used as variables in predicting fever onset in logistic regression analysis. The variables included were maximum temperature within 24 hours, minimum temperature within 24 hours, average of positive increases between subsequent measurements, and largest 24 hour increase. Statistical analysis consisted of a generalized linear model with logit link (logistic regression) predicting fever at least 4 hours before onset, and used generalized estimating equations to adjust for correlated temperature measures within patient. Results Of the 68 patients identified, 47% were male, 53% were female with a mean age of 56.3 ± 15.1 years. Our fever curve plots suggest that there is an increase in average temperature at least 24 hours before the onset of fever in those patients that will go on to develop a fever by current definition (Figure 1). A prediction score including, maximum temperature within 24 hours, minimum temperature within 24 hours, average of positive increases between subsequent measurements, and largest 24 hour increase was able to predict 86.1% of oncoming FN events 4 to 28 hours before onset and reject 67.4% of non-FN events. This rule has a negative predictive value of 96.2% and a positive predictive value of 33.7%. Discussion Our analysis demonstrates the feasibility of using temperature series data for early prediction of FN. A more comprehensive analysis is planned and is expected to result in higher sensitivities. If subsequent analysis proves to be significant this data may be used to develop future prospective clinical studies to evaluate new fever criteria and may alter our current definition and management of pts with FN. Figure 1. 4-days of temperature series data preceding onset of fever or end of series if no fever. The dark lines are LOESS smoothed average temperatures for series ending in fever (dash) or non-fever (solid). Figure 1. 4-days of temperature series data preceding onset of fever or end of series if no fever. The dark lines are LOESS smoothed average temperatures for series ending in fever (dash) or non-fever (solid). Disclosures No relevant conflicts of interest to declare.

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A statistical bias correction technique for global climate model predicted near-surface temperature in India using the generalized regression neural network

Journal of Water and Climate Change ◽

10.2166/wcc.2022.214 ◽

2022 ◽

Author(s):

Diljit Dutta ◽

Rajib Kumar Bhattacharjya

Keyword(s):

Neural Network ◽

Surface Temperature ◽

Bias Correction ◽

Global Climate ◽

Temperature Data ◽

Generalized Regression Neural Network ◽

Emission Scenarios ◽

Statistical Bias ◽

Near Surface ◽

Statistical Bias Correction

Abstract Global climate models (GCMs) developed by the numerical simulation of physical processes in the atmosphere, ocean, and land are useful tools for climate prediction studies. However, these models involve parameterizations and assumptions for the simulation of complex phenomena, which lead to random and structural errors called biases. So, the GCM outputs need to be bias-corrected with respect to observed data before applying these model outputs for future climate prediction. This study develops a statistical bias correction approach using a four-layer feedforward radial basis neural network – a generalized regression neural network (GRNN) to reduce the biases of the near-surface temperature data in the Indian mainland. The input to the network is the CNRM-CM5 model output gridded data of near-surface temperature for the period 1951–2005, and the target to the model used for bias correcting the input data is the gridded near-surface temperature developed by the Indian Meteorological Department for the same period. Results show that the trained GRNN model can improve the inherent biases of the GCM modelled output with significant accuracy, and a good correlation is seen between the test statistics of observed and bias-corrected data for both the training and testing period. The trained GRNN model developed is then used for bias correction of CNRM-CM5 modelled projected near-surface temperature for 2006–2100 corresponding to the RCP4.5 and RCP8.5 emission scenarios. It is observed that the model can adapt well to the nature of unseen future temperature data and correct the biases of future data, assuming quasi-stationarity of future temperature data for both emission scenarios. The model captures the seasonal variation in near-surface temperature over the Indian mainland, having diverse topography appreciably, and this is evident from the bias-corrected output.

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