scholarly journals Precipitation phase uncertainty in cold region conceptual models resulting from meteorological forcing time-step intervals

2020 ◽  
Vol 51 (2) ◽  
pp. 180-187
Author(s):  
James M. Feiccabrino
Keyword(s):  
Air Temperature ◽  
Climate Models ◽  
Phase Error ◽  
Dew Point ◽  
Time Step ◽  
Meteorological Forcing ◽  
Surface Precipitation ◽  
Phase Uncertainty ◽  
Meteorological Observations ◽  
Precipitation Phase

Abstract Precipitation phase determination is a known source of uncertainty in surface-based hydrological, ecological, safety, and climate models. This is primarily due to the surface precipitation phase being a result of cloud and atmospheric properties not measured at surface meteorological or hydrological stations. Adding to the uncertainty, many conceptual hydrological models use a 24-h average air temperature to determine the precipitation phase. However, meteorological changes to atmospheric properties that control the precipitation phase often substantially change at sub-daily timescales. Model uncertainty (precipitation phase error) using air temperature (AT), dew-point temperature (DP), and wet-bulb temperature (WB) thresholds were compared using averaged and time of observation readings at 1-, 3-, 6-, 12-, and 24-h periods. Precipitation phase uncertainty grew 35–65% from the use of 1–24 h data. Within a sub-dataset of observations occurring between AT −6 and 6 °C representing 57% of annual precipitation, misclassified precipitation was 7.9% 1 h and 11.8% 24 h. Of note, there was also little difference between 1 and 3 h uncertainty, typical time steps for surface meteorological observations.

Surface-based precipitation phase determination methods in hydrological models

2012 ◽  
Vol 44 (1) ◽  
pp. 44-57 ◽  
Author(s):  
James Feiccabrino ◽  
David Gustafsson ◽  
Angela Lundberg
Keyword(s):  
Air Temperature ◽  
Dew Point ◽  
Temperature Threshold ◽  
Phase Determination ◽  
Probability Curve ◽  
Linear Decrease ◽  
Meteorological Observations ◽  
Second Category ◽  
Model Precipitation ◽  
Precipitation Phase

We compared solid and liquid precipitation mass output from three categories of common model precipitation phase determination schemes (PPDS) to the recorded precipitation phase in a set of 45 years of 3-hour manual meteorological observations from 19 Swedish meteorological stations. In the first category of rain/snow thresholds, it was found that rain/snow air temperature threshold (ATT) is a better precipitation phase indicator than a rain/snow dew point temperature threshold. When a rain/snow ATT of 0.0 °C (a default value used in some recent models) was replaced by 1.0 °C, misclassified precipitation was reduced by almost one half. A second category of PPDS use two ATTs, one snow and one rain, with a linear decrease in snow fraction between. This category identified precipitation phase better than a rain/snow ATT at 17 stations. Using all observations from all the meteorological stations, a final category using an air-temperature-dependent snow probability curve resulted in slightly lower misclassified precipitation mass at 13 of the 19 stations. However, schemes from the linear decrease in snow fraction category had the lowest misclassified precipitation mass at four meteorological stations.

Using the ERA5 dataset of atmospheric variables to estimate daily reference evapotranspiration in Sicily, Italy.

2021 ◽  
Author(s):  
Giuseppe Provenzano ◽  
Matteo Ippolito
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Air Temperature ◽  
Management Practices ◽  
Reference Evapotranspiration ◽  
Dew Point ◽  
Climate Data ◽  
Crop Evapotranspiration ◽  
Time Step ◽  
Mediterranean Countries

<p>Crop evapotranspiration (ET) plays a key role in many hydrological processes involving the soil-plant-atmosphere system. The concept of reference crop evapotranspiration (ET<sub>0</sub>) was introduced to estimate the atmosphere evaporation demand independently of crop type, development stage and management practices. Among the available methods to estimate ET<sub>0</sub>, the Penman-Monteith equation proposed by the Food and Agriculture Organization of the United Nations (FAO56-PM), is considered one of the most accurate, so that it is assumed as a reference to calibrate other simplified procedures. In several regions of the world, the limited availability of meteorological observations to estimate ET<sub>0</sub> can be overcome by using gridded reanalysis dataset created by data assimilation of weather observations. Different datasets with relatively high spatial resolution but different in terms of Spatio-temporal resolution have been generated and are freely downloadable at the global scale. The latest ERA5-Land product released in 2019 is characterized by a spatial grid to 0.1° latitude and 0.1° longitude. The database provides several land variables at hourly time-step including, among others, air temperature, dew point temperature and solar radiation at 2.0 m above the soil surface, as well as the wind speed components at 10 m height.</p><p>The objective of the research was to assess the suitability of ERA5-Land dataset of climate data to predict daily reference evapotranspiration in Sicily, Italy. For the period 2006-2015, the performance of the reanalysis data to capture the local climate variables was assessed based on the comparison with the corresponding ground data measured by a network of 39 climate stations in Sicily belonging to the Agrometeorological Information Service (SIAS). After evaluating the statistical errors associated with each climatic variables retrieved from the ERA5-Land, the comparison between daily ET<sub>0</sub> values obtained with the FAO56-PM and considering both the dataset was carried out.</p><p>The analysis showed that air temperature, solar radiation and wind speed retrieved by the ERA-5 dataset resulted in quite good agreement with the corresponding measured on the ground, with an average root mean square error (RMSE) equal respectively to 1.8°C, 2.9 MJm<sup>-2</sup>d<sup>-1</sup>, and 1.3 ms<sup>-1</sup> and corresponding mean bias errors (MBE) of -0.4°C, 1.0 MJm<sup>-2</sup>d<sup>-1</sup>  and -0.1 ms<sup>-1</sup>. On the other hand, relative air humidity was characterized by average values of RMSE and MBE respectively equal to 10.3% and 5.6%. When considering all the examined climate stations, the RMSE and MBE values associated with ET<sub>0</sub> ranged from 0.4 to 1.3 mm d<sup>-1</sup>, and -1.0 and 0.0 mm d<sup>-1</sup>, supporting the possibility to consider the ERA-5 data to obtain suitable estimations of crop reference evapotranspiration even for other Mediterranean countries where measured climate data are not available.</p>

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals Solar Radiation, Air Temperature, Relative Humidity, and Dew Point Study: Damak, Jhapa, Nepal

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Arun Kumar Shrestha ◽  
Arati Thapa ◽  
Hima Gautam
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Air Temperature ◽  
Gaussian Function ◽  
Great Influence ◽  
Dew Point ◽  
Upward Trend ◽  
Summer Maximum ◽  
Online Calculator ◽  
Climatic Phenomenon

Monitoring and prediction of the climatic phenomenon are of keen interest in recent years because it has great influence in the lives of people and their environments. This paper is aimed at reporting the variation of daily and monthly solar radiation, air temperature, relative humidity (RH), and dew point over the year of 2013 based on the data obtained from the weather station situated in Damak, Nepal. The result shows that on a clear day, the variation of solar radiation and RH follows the Gaussian function in which the first one has an upward trend and the second one has a downward trend. However, the change in air temperature satisfies the sine function. The dew point temperature shows somewhat complex behavior. Monthly variation of solar radiation, air temperature, and dew point shows a similar pattern, lower at winter and higher in summer. Maximum solar radiation (331 Wm-2) was observed in May and minimum (170 Wm-2) in December. Air temperature and dew point had the highest value from June to September nearly at 29°C and 25°C, respectively. The lowest value of the relative humidity (55.4%) in April indicates the driest month of the year. Dew point was also calculated from the actual readings of air temperature and relative humidity using the online calculator, and the calculated value showed the exact linear relationship with the observed value. The diurnal and nocturnal temperature of each month showed that temperature difference was relatively lower (less than 10°C) at summer rather than in winter.

Measurement of indoor environmental parameters and analysis of the condensation phenomenon in urban utility tunnels

2022 ◽  
pp. 1420326X2110564
Author(s):  
Chuanmin Tai ◽  
Guansan Tian ◽  
Wenjun Lei
Keyword(s):  
Water Supply ◽  
Surface Temperature ◽  
Air Temperature ◽  
Natural Ventilation ◽  
Environmental Parameters ◽  
Dew Point ◽  
Control Mode ◽  
Safe Management ◽  
Environmental Test ◽  
Water Supply Pipeline

Condensation is a major issue in the safe operation of utility tunnels. To address the condensation problem, the indoor air temperature, relative humidity (RH) and surface temperature in an urban utility tunnel in Jining were continuously measured, and the condensation conditions were surveyed and analysed. The results indicated that under natural ventilation conditions, the air temperature in the comprehensive cabin varied from 23.4°C to 24.5°C, the RH fluctuated between 86.4% and 95.3%, and the corresponding air dew point temperature (DPT) remained in the range of 22.2°C–22.9°C. The surface temperature of the water supply pipeline ranged from 17.8°C to 18.5°C, which was far lower than the DPT in the tunnel, resulting in serious condensation. A water supply pipeline with an anti-condensation design was developed based on environmental test data. A 25-mm-thick rubber plastic sponge insulation layer was used to thermally insulate the water supply pipeline, preventing further dew condensation. Furthermore, mechanical ventilation had little effect on reducing the RH in the tunnel and may actually cause dew condensation; therefore, a ventilation control mode was proposed in this study. These results are expected to provide basic data for further research and reference for the safe management of utility tunnels.

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>

Impact of climate change on water availability in Tropical Andean catchments: a case study in the Vilcanota catchment, Peru

2021 ◽  
Author(s):  
Selina Meier ◽  
Randy Munoz ◽  
Christian Huggel
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Water Demand ◽  
Climate Models ◽  
Local Population ◽  
Time Step ◽  
Glacier Surface ◽  
Hydrological Simulation ◽  
Sustainable Water Management ◽  
Monthly Runoff

<p>Water scarcity is increasingly becoming a problem in many regions of the world. On the one hand, this can be attributed to changes in precipitation conditions due to climate change. On the other hand, this is also due to population growth and changes in consumer behaviour. In this study, an analysis is carried out for the highly glaciated Vilcanota River catchment (9808 km<sup>2</sup> – 1.2% glacier area) in the Cusco region (Peru). Possible climatic and socioeconomic scenarios up to 2050 were developed including the interests from different water sectors, i.e. agriculture, domestic and energy.</p><p>The analysis consists of the hydrological simulation at a monthly time step from September 2043 to August 2050 using a simple glacio-hydrological model. For historic conditions (1990 to 2006) a combination of gridded data (PISCO precipitation) and weather stations was used. Future scenario simulations were based on three different climate models for both RCP 2.6 and 8.5. Different glacier outlines were used to simulate changes in glacier surface through the time for both historic (from satellite data) and future (from existing literature) scenarios. Furthermore, future water demand simulations were based on the SSP1 and SSP3 scenarios.</p><p>Results from all scenarios suggest an average monthly runoff of about 130 m<sup>3</sup>/s for the Vilcanota catchment between 2043 and 2050. This represents a change of about +5% compared to the historical monthly runoff of about 123 m<sup>3</sup>/s. The reason for the increase in runoff is related to the precipitation data from the selected climate models. However, an average monthly deficit of up to 50 m<sup>3</sup>/s was estimated between April and November with a peak in September. The seasonal deficit is related to the seasonal change in precipitation, while the water demand seems to have a less important influence.</p><p>Due to the great uncertainty of the modelling and changes in the socioeconomic situation, the data should be continuously updated. In order to construct a locally sustainable water management system, the modelling needs to be further downscaled to the different subcatchments in the Vilcanota catchment. To address the projected water deficit, a new dam could partially compensate for the decreasing storage capacity of the melting glaciers. However, the construction of the dam could meet resistance from the local population if they cannot be promised and communicated multiple uses of the new dam. Sustainable water management requires the cooperation of all stakeholders and all stakeholders should be able to benefit from it so that they will support future projects.</p>

Modeling impacts of super cool roofs on air temperature at pedestrian level in mesoscale and microscale climate models

Urban Climate ◽  
2021 ◽  
Vol 40 ◽  
pp. 101001
Author(s):  
Tim Sinsel ◽  
Helge Simon ◽  
Ashley M. Broadbent ◽  
Michael Bruse ◽  
Jannik Heusinger
Keyword(s):  
Air Temperature ◽  
Climate Models ◽  
Cool Roofs ◽  
Super Cool
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