Evaluation of multisite synthetic data generated by spatial weather generator and long climate data series

In this paper a new validation test for the spatial weather generator SWGEN producing the multisite daily time series of solar radiation, temperature and precipitation is presented. The method was tested by comparing statistics of 1000 years of generated data with extra long series of 35 years of observed weather parameters and 24 sites of meteorological stations for south-west Poland. The method evaluation showed that the means (sums) and variances of generated data were comparable with observed climatic data aggregated for months, seasons and years.

Download Full-text

Example of analysis of climatic data series with respect to the testing of reinforcement concrete corrosion in a climate chamber

MATEC Web of Conferences ◽

10.1051/matecconf/202031300037 ◽

2020 ◽

Vol 313 ◽

pp. 00037

Author(s):

Petr Lehner ◽

Petr Konečný ◽

Ryszard Walentyński

Keyword(s):

Statistical Evaluation ◽

Data Series ◽

Rapid Evaluation ◽

Climatic Data ◽

Climate Data ◽

Climate Chamber ◽

Leos Janacek ◽

Concrete Corrosion ◽

Set Up ◽

Reinforced Concrete Corrosion

The paper presents a possible statistical evaluation of the climate data, namely temperature and relative humidity, with respect to the rapid evaluation of the risk of reinforced concrete corrosion in the laboratory conditions. Data on temperature and humidity from Leoš Janáček Ostrava Airport over the last ten years are analysed. The processed data will be used as the set up for the climate chamber where the concrete samples with steel rods will be placed.

Download Full-text

Twentieth-century variations in temperature and precipitation in the Nordic Arctic

Polar Record ◽

10.1017/s0032247400017721 ◽

2002 ◽

Vol 38 (206) ◽

pp. 203-210 ◽

Cited By ~ 20

Author(s):

E. J. Førland ◽

I. Hanssen-Bauer ◽

T. Jónsson ◽

C. Kern-Hansen ◽

P.Ø. Nordli ◽

...

Keyword(s):

Twentieth Century ◽

Temperature Trend ◽

Annual Precipitation ◽

Data Series ◽

Positive Trend ◽

Positive Temperature ◽

Climate Data ◽

Data Set ◽

Temperature And Precipitation

AbstractIn a joint Nordic effort, a high-quality climate data set for the Nordic Arctic is established. The data set consists of monthly values from 20 stations in Greenland, Iceland, the Faeroes, and the Norwegian Arctic. The data set is made available on the web. Ten climate elements are included, and most of the series covers the period 1890–2000. The data series illustrate the large climatic contrasts in the Nordic Arctic, and demonstrate that parts of the region have experienced substantial climate variations during the last century. Despite increasing temperatures during recent decades, the present temperature level is still lower than in the 1930s and 1950s in large parts of the region. The pattern of long-term precipitation variations is more complicated, but in parts of the region the annual precipitation has increased substantially. At Svalbard Airport and Bjørnøya the annual precipitation has increased by more than 2.5% per decade during the twentieth century.Variations in atmospheric circulation can account for most of the long-term positive trend in precipitation in the Norwegian Arctic, and also for the positive temperature trend from the 1960s. The positive temperature trend before 1930 and the negative trend during the following decades, are, however, not accounted for by the circulation models.

Download Full-text

Is it feasible to estimate radiosonde biases from interlaced measurements?

Atmospheric Measurement Techniques ◽

10.5194/amt-11-3021-2018 ◽

2018 ◽

Vol 11 (5) ◽

pp. 3021-3029

Author(s):

Stefanie Kremser ◽

Jordis S. Tradowsky ◽

Henning W. Rust ◽

Greg E. Bodeker

Keyword(s):

Climate Change ◽

Global Climate ◽

Synthetic Data ◽

Internal Variability ◽

Data Series ◽

Data Sets ◽

Climate Variables ◽

Climate Data ◽

Reference Quality ◽

New Instruments

Abstract. Upper-air measurements of essential climate variables (ECVs), such as temperature, are crucial for climate monitoring and climate change detection. Because of the internal variability of the climate system, many decades of measurements are typically required to robustly detect any trend in the climate data record. It is imperative for the records to be temporally homogeneous over many decades to confidently estimate any trend. Historically, records of upper-air measurements were primarily made for short-term weather forecasts and as such are seldom suitable for studying long-term climate change as they lack the required continuity and homogeneity. Recognizing this, the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) has been established to provide reference-quality measurements of climate variables, such as temperature, pressure, and humidity, together with well-characterized and traceable estimates of the measurement uncertainty. To ensure that GRUAN data products are suitable to detect climate change, a scientifically robust instrument replacement strategy must always be adopted whenever there is a change in instrumentation. By fully characterizing any systematic differences between the old and new measurement system a temporally homogeneous data series can be created. One strategy is to operate both the old and new instruments in tandem for some overlap period to characterize any inter-instrument biases. However, this strategy can be prohibitively expensive at measurement sites operated by national weather services or research institutes. An alternative strategy that has been proposed is to alternate between the old and new instruments, so-called interlacing, and then statistically derive the systematic biases between the two instruments. Here we investigate the feasibility of such an approach specifically for radiosondes, i.e. flying the old and new instruments on alternating days. Synthetic data sets are used to explore the applicability of this statistical approach to radiosonde change management.

Download Full-text

Climatic variability at Gangtok and Tadong weather observatories in Sikkim, India, during 1961–2017

Scientific Reports ◽

10.1038/s41598-020-71163-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Parvendra Kumar ◽

Milap Chand Sharma ◽

Rakesh Saini ◽

Girish Kumar Singh

Keyword(s):

Extreme Events ◽

Climatic Variability ◽

Eastern Himalaya ◽

Data Series ◽

Climatic Data ◽

Positive Trend ◽

Temperature And Precipitation ◽

Cusum Charts ◽

Slope Estimator ◽

Maximum Temperatures

Abstract The present study documents the long-term trends in the temperature and precipitation of a poorly represented region, the Sikkim, eastern Himalaya using the Mann–Kendall non-parametric test and the Sen’s slope estimator. Additionally, the normal distribution curves and Cusum charts have been used to identify the shifts in extreme events and to detect the points of change in the climatic data series for robust analysis. The minimum temperatures recorded a positive trend in Gangtok (0.036 ˚C year−1 from 1961 to 2017) as well as in Tadong (0.065 ˚C year−1 from 1981 to 2010) stations, while the maximum temperatures showed no trend in Tadong station from 1981 to 2010 which is consistent with the trend in Gangtok station for the overlapped period. However, it was negative for the overall assessed period (− 0.027 ˚C year−1 from 1961 to 2017) in Gangtok. The average temperatures in Gangtok recorded no trend whereas a positive trend (0.035 ˚C year−1 from 1981 to 2010) was observed at Tadong station. A similar positive trend in the average temperatures has been detected at Gangtok also for the overlapped period. Accelerated warming was noticed during the last two decades with an increase in the probability of extreme events of temperatures (minimum, maximum, average) at the higher end. Precipitation was found to be more variable across the observed period and suggested no trend in the study area.

Download Full-text

Creating a Serially Complete, National Daily Time Series of Temperature and Precipitation for the Western United States

Journal of Applied Meteorology ◽

10.1175/1520-0450(2000)039<1580:cascnd>2.0.co;2 ◽

2000 ◽

Vol 39 (9) ◽

pp. 1580-1591 ◽

Cited By ~ 126

Author(s):

Jon K. Eischeid ◽

Phil A. Pasteris ◽

Henry F. Diaz ◽

Marc S. Plantico ◽

Neal J. Lott

Keyword(s):

United States ◽

Time Series ◽

Western United States ◽

Temperature And Precipitation ◽

Daily Time Series ◽

Daily Time

Download Full-text

Is it feasible to estimate radiosonde biases from interlaced measurements?

10.5194/amt-2018-6 ◽

2018 ◽

Author(s):

Stefanie Kremser ◽

Jordis S. Tradowsky ◽

Henning W. Rust ◽

Greg E. Bodeker

Keyword(s):

Climate Change ◽

Global Climate ◽

Synthetic Data ◽

Internal Variability ◽

Data Series ◽

Data Sets ◽

Climate Variables ◽

Climate Data ◽

Reference Quality ◽

New Instruments

Abstract. Upper-air measurements of essential climate variables (ECVs), such as temperature, are crucial for climate monitoring and climate change detection. Because of the internal variability of the climate system, many decades of measurements are typically required to robustly detect any trend in the climate data record. It is imperative for the records to be temporally homogeneous over many decades to confidently estimate any trend. Historically, records of upper-air measurements were primarily made for short-term weather forecasts and, as such, are seldom suitable for studying long-term climate change as they lack the required continuity and homogeneity. Recognizing this, the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) has been established to provide reference-quality measurements of climate variables, such as temperature, pressure and humidity, together with well characterized and traceable estimates of the measurement uncertainty. To ensure that GRUAN data products are suitable to detect climate change, a scientifically robust instrument replacement strategy must always be adopted whenever there is a change in instrumentation. By fully characterizing any systematic differences between the old and new measurement system a temporally homogeneous data series can be created. One strategy is to operate both the old and new instruments in tandem for some overlap period to characterize any inter-instrument biases. However, this strategy can be prohibitively expensive measurement sites operated by national weather services or research institutes. An alternative strategy that has been proposed is to alternate between the old and new instruments, so-called interlacing, and then statistically derive the systematic biases between the two instruments. Here we investigate the feasibility of such an approach specifically for radiosondes, i.e. flying the old and new instruments on alternating days. Synthetic data sets are used to explore the applicability of this statistical approach to radiosonde change management.

Download Full-text

Application of spatial weather generator for the assessment of climate change impacts on a river runoff

Geografie ◽

10.37040/geografie2014119010001 ◽

2014 ◽

Vol 119 (1) ◽

pp. 1-25 ◽

Cited By ~ 8

Author(s):

Leszek Kuchar ◽

Sławomir Iwański ◽

Leszek Jelonek ◽

Wiwiana Szalińska

Keyword(s):

Climate Change ◽

Hydrological Model ◽

Climate Change Impacts ◽

Weather Generator ◽

Time Step ◽

Weather Patterns ◽

Global Circulation Models ◽

Temperature And Precipitation ◽

Projected Changes ◽

Daily Time

In this study, the impacts of climate change on streamflow are investigated. The ensemble of outputs from three different Global Circulation Models models: GISS, CCCM, GFDL developed for the emission scenario A1B were analyzed to infer projected changes in climatological conditions for the region of the Upper and Middle Odra basin. Obtaining hydrological scenarios of future changes for the scale of subcatchment required simulating short-term and fine scaled weather patterns for this area. SWGEN model (Spatial Weather GENerator) was applied to downscale projected changes of climatological conditions to the ones required by hydrological model temporal and spatial resolution. Daily time series of solar radiation, temperature and precipitation were generated for the reference period 1981–2000 and for the time horizon 2030 and 2050. The generated data from SWGEN model were integrated in the hydrological model NAM to simulate streamflow under changed conditions with daily time step. The results show considerable changes in annual and seasonal runoff daily distributions for selected study catchment in the future time horizons of 2030 and 2050.

Download Full-text

Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020

Epidemiologia ◽

10.3390/epidemiologia2010009 ◽

2021 ◽

Vol 2 (1) ◽

pp. 95-113 ◽

Cited By ~ 1

Author(s):

Isaac Chun-Hai Fung ◽

Xiaolu Zhou ◽

Chi-Ngai Cheung ◽

Sylvia K. Ofori ◽

Kamalich Muniz-Rodriguez ◽

...

Keyword(s):

Regression Models ◽

Doubling Time ◽

Metropolitan Areas ◽

Mainland China ◽

Linear Regression Models ◽

Case Count ◽

Daily Time Series ◽

The Mean ◽

Doubling Times ◽

Daily Time

To describe the geographical heterogeneity of COVID-19 across prefectures in mainland China, we estimated doubling times from daily time series of the cumulative case count between 24 January and 24 February 2020. We analyzed the prefecture-level COVID-19 case burden using linear regression models and used the local Moran’s I to test for spatial autocorrelation and clustering. Four hundred prefectures (~98% population) had at least one COVID-19 case and 39 prefectures had zero cases by 24 February 2020. Excluding Wuhan and those prefectures where there was only one case or none, 76 (17.3% of 439) prefectures had an arithmetic mean of the epidemic doubling time <2 d. Low-population prefectures had a higher per capita cumulative incidence than high-population prefectures during the study period. An increase in population size was associated with a very small reduction in the mean doubling time (−0.012, 95% CI, −0.017, −0.006) where the cumulative case count doubled ≥3 times. Spatial analysis revealed high case count clusters in Hubei and Heilongjiang and fast epidemic growth in several metropolitan areas by mid-February 2020. Prefectures in Hubei and neighboring provinces and several metropolitan areas in coastal and northeastern China experienced rapid growth with cumulative case count doubling multiple times with a small mean doubling time.

Download Full-text