scholarly journals Improving surface-based precipitation phase determination through air mass boundary identification

2012 ◽  
Vol 43 (3) ◽  
pp. 179-191 ◽  
Author(s):  
James Feiccabrino ◽  
Angela Lundberg ◽  
David Gustafsson
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Lower Atmosphere ◽  
Temperature Threshold ◽  
Hydrological Models ◽  
Atmospheric Conditions ◽  
Threshold Method ◽  
Phase Determination ◽  
Air Mass ◽  
Precipitation Phase

Most hydrological models apply one empirical formula based on surface air temperature for precipitation phase determination. This approach is flawed as surface precipitation phase results from energy exchanges between falling precipitation and air in the lower atmosphere. Different lower atmospheric conditions cause different precipitation phase probabilities for near-freezing temperatures. Often directly measured lower atmospheric conditions are not available for remote areas. However, meteorological observations occurring before/after similar air mass boundaries have similar atmospheric conditions that vary from most other observations. Therefore, hydrological models can indirectly account for lower atmospheric conditions. Twenty years of manual observations from eight United States weather stations were used to compare misclassified precipitation proportions when analyzing (a) all precipitation observations together and (b) identified cold air mass boundary observations (CAB) and non-CAB observations separately. The CAB observations were identified by a rapid surface air temperature decrease. A two-surface air temperature threshold method with one threshold all snow (TS°C) and one all rain (TR°C) having a linear snow fraction decrease between the thresholds was used. The TS (0 °C), and TR (4 °C) values for CAB were 1 °C warmer than for non-CAB (−1 °C, 3 °C). Analyzing CAB and non-CAB separately reduced misclassified precipitation 23%, from 7.0 to 5.4%.

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.

scholarly journals Sensitivity of Precipitation Phase over the Swiss Alps to Different Meteorological Variables

2014 ◽  
Vol 15 (2) ◽  
pp. 685-696 ◽  
Author(s):  
S. Froidurot ◽  
I. Zin ◽  
B. Hingray ◽  
A. Gautheron
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Data ◽  
Explanatory Power ◽  
Surface Air Temperature ◽  
Ground Level ◽  
Swiss Alps ◽  
Meteorological Variables ◽  
Study Region ◽  
Precipitation Phase

Abstract In most meteorological or hydrological models, the distinction between snow and rain is based only on a given air temperature. However, other factors such as air moisture can be used to better distinguish between the two phases. In this study, a number of models using different combinations of meteorological variables are tested to determine their pertinence for the discrimination of precipitation phases. Spatial robustness is also evaluated. Thirty years (1981–2010) of Swiss meteorological data are used, consisting of radio soundings from Payerne as well as present weather observations and surface measurements (mean hourly surface air temperature, mean hourly relative humidity, and hourly precipitation) from 14 stations, including Payerne. It appeared that, unlike surface variables, variables derived from the atmospheric profiles (e.g., the vertical temperature gradient) hardly improve the discrimination of precipitation phase at ground level. Among all tested variables, surface air temperature and relative humidity show the greatest explanatory power. The statistical model using these two variables and calibrated for the case study region provides good spatial robustness over the region. Its parameters appear to confirm those defined in the model presented by Koistinen and Saltikoff.

scholarly journals Growing Season Air mass Equivalent Temperature (TE) in the East Central USA

Climate ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 95
Author(s):  
Dolly Na-Yemeh ◽  
Rezaul Mahmood ◽  
Gregory Goodrich ◽  
Keri Younger ◽  
Kevin Cary ◽  
...  
Keyword(s):  
Heat Content ◽  
Surface Air Temperature ◽  
Growing Season ◽  
Atmospheric Conditions ◽  
Equivalent Temperature ◽  
Air Mass ◽  
Air Masses ◽  
Growing Seasons ◽  
The Difference ◽  
June July

Equivalent temperature (TE), which incorporates both dry (surface air temperature, T) and moist heat content associated with atmospheric moisture, is a better indicator of overall atmospheric heat content compared to T alone. This paper investigates the impacts of different types of air masses on TE during the growing season (April–September). The study used data from the Kentucky Mesonet for this purpose. The growing season was divided into early (April–May), mid (June–July), and late (August–September). Analysis suggests that TE for moist tropical (MT) air mass was as high as 61 and 81 °C for the early and mid-growing season, respectively. Further analysis suggests that TE for different parts of the growing seasons were statistically significantly different from each other. In addition, TE for different air masses was also statistically significantly different from each other. The difference between TE and T (i.e. TE-T) is smaller under dry atmospheric conditions but larger under moist conditions. For example, in Barren County, the lowest difference (20–10 °C) was 10 °C. It was reported on 18 April 2010, a dry weather day. On the other hand, the highest difference for this site was 48 °C and was reported on 11 August 2010, a humid day.

scholarly journals Prediction of Arctic temperature and sea ice using a high-resolution coupled model

2021 ◽  
pp. 1-62
Author(s):  
Le Chang ◽  
Jing-Jia Luo ◽  
Jiaqing Xue ◽  
Haiming Xu ◽  
Nick Dunstone
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Air Temperature ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Arctic Region ◽  
Warming Trend ◽  
Lower Atmosphere ◽  
The Arctic

AbstractUnder global warming, surface air temperature has risen rapidly and sea ice decreased markedly in the Arctic. These drastic climate changes have brought about various severe impacts on the vulnerable environment and ecosystem there. Thus, accurate prediction of Arctic climate becomes more important than before. Here we examine the seasonal to interannual predictive skills of 2-meter air temperature (2-m T) and sea ice cover (SIC) over the Arctic region (70°∼90°N) during 1980–2014 with a high-resolution global coupled model called the Met Office Decadal Prediction System version 3 (DePreSys3). The model captures well both the climatology and interannual variability of the Arctic 2-m T and SIC. Moreover, the anomaly correlation coefficient (ACC) of Arctic-averaged 2-m T and SIC shows statistically significant skills at lead times up to 16 months. This is mainly due to the contribution of strong decadal trends. In addition, it is found that the peak warming trend of Arctic 2-m T lags the maximum decrease trend of SIC by one month, in association with the heat flux forcing from the ocean surface to lower atmosphere. While the predictive skill is generally much lower for the detrended variations, we find a close relationship between the tropical Pacific El Niño–Southern Oscillation and the Arctic detrended 2-m T anomalies. This indicates potential seasonal to interannual predictability of the Arctic natural variations.

scholarly journals A Detailed Cloud Fraction Climatology of the Upper Indus Basin and Its Implications for Near-Surface Air Temperature*

2015 ◽  
Vol 28 (9) ◽  
pp. 3537-3556 ◽  
Author(s):  
N. Forsythe ◽  
A. J. Hardy ◽  
H. J. Fowler ◽  
S. Blenkinsop ◽  
C. G. Kilsby ◽  
...  
Keyword(s):  
Air Temperature ◽  
Cloud Cover ◽  
Large Fraction ◽  
Surface Air Temperature ◽  
Indus Basin ◽  
Radiative Effect ◽  
Atmospheric Conditions ◽  
Near Surface ◽  
Cloud Radiative Effect ◽  
Seasonal And Diurnal Variations

Abstract Clouds play a key role in hydroclimatological variability by modulating the surface energy balance and air temperature. This study utilizes MODIS cloud cover data, with corroboration from global meteorological reanalysis (ERA-Interim) cloud estimates, to describe a cloud climatology for the upper Indus River basin. It has specific focus on tributary catchments in the northwest of the region, which contribute a large fraction of basin annual runoff, including 65% of flow originating above Besham, Pakistan or 50 km3 yr−1 in absolute terms. In this region there is substantial cloud cover throughout the year, with spatial means of 50%–80% depending on the season. The annual cycles of catchment spatial mean daytime and nighttime cloud cover fraction are very similar. This regional diurnal homogeneity belies substantial spatial variability, particularly along seasonally varying vertical profiles (based on surface elevation). Correlations between local near-surface air temperature observations and MODIS cloud cover fraction confirm the strong linkages between local atmospheric conditions and near-surface climate variability. These correlations are interpreted in terms of seasonal and diurnal variations in apparent cloud radiative effect and its influence on near-surface air temperature in the region. The potential role of cloud radiative effect in recognized seasonally and diurnally asymmetrical temperature trends over recent decades is also assessed by relating these locally observed trends to ERA-Interim-derived trends in cloud cover fraction. Specifically, reduction in nighttime cloud cover fraction relative to daytime conditions over recent decades appears to provide a plausible physical mechanism for the observed nighttime cooling of surface air temperature in summer months.

scholarly journals Atmospheric Processes and Climatological Characteristics of the 79N Glacier (Northeast Greenland)

2019 ◽  
Vol 147 (4) ◽  
pp. 1375-1394 ◽  
Author(s):  
Jenny V. Turton ◽  
Thomas Mölg ◽  
Dirk Van As
Keyword(s):  
Air Temperature ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Greenland Ice Sheet ◽  
Cloud Formation ◽  
Atmospheric Conditions ◽  
Subsequent Increase ◽  
Near Surface ◽  
Surface Mass ◽  
Atmospheric Processes

Abstract The Nioghalvfjerdsfjorden glacier (the 79 fjord, henceforth referred to as 79N) has been thinning and accelerating since the early 2000s, as a result of calving episodes at the front of the glacier. As 8% of the Greenland Ice Sheet area drains into 79N, changes in the stability of 79N could propagate into the interior of Greenland. Despite this concern, relatively little is known about the atmospheric conditions over 79N. We present the surface atmospheric processes and climatology of the 79N region from analyses of data from four automatic weather stations (AWS) and reanalysis data from ERA-Interim. Over the floating section of the glacier, the annual average air temperature is −16.7°C, decreasing to −28.5°C during winter. Winds over the glacier are predominantly westerly and are of katabatic origin. Over the last 39 years the near-surface air temperature has increased at a rate of +0.08°C yr−1. In addition, we find that large, rapid (48 h) temperature increases (>10°C) occur during the five-month dark period (November–March). Eight (±4) warm-air events occur annually from 1979 to 2017. We use the Weather Research and Forecasting (WRF) Model to simulate a particular warm-air event with above-freezing air temperatures between 30 November and 2 December 2014. The warm event was caused by warm-air advection from the southeast and a subsequent increase in the longwave radiation toward the surface due to low-level cloud formation. The frequent nature of the temperature jumps and the magnitude of the temperature increases are likely to have an impact on the surface mass balance of the glacier by bringing the skin temperatures to the melting point.

scholarly journals Reducing misclassified precipitation phase in conceptual models using cloud base heights and relative humidity to adjust air temperature thresholds

2021 ◽  
Author(s):  
Lic James M. Feiccabrino
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Conceptual Models ◽  
Phase Changes ◽  
Cloud Base ◽  
Temperature Threshold ◽  
Air Temperatures ◽  
Low Cloud ◽  
Precipitation Phase ◽  
Cloud Base Height

Abstract In cold region, conceptual models assigned precipitation phase, liquid (rain) or solid (snow), cause vastly different atmospheric, hydrological, and ecological responses, along with significant differences in evaporation, runoff, and infiltration fates for measured precipitation mass. A set air temperature threshold (ATT) applied to the over 30% annual precipitation events occurring with surface air temperatures between −3 and 5 °C resulted in 11.0 and 9.8% misclassified precipitation in Norway and Sweden, respectively. Surface air temperatures do not account for atmospheric properties causing precipitation phase changes as snow falls toward the ground. However, cloud base height and relative humidity (RH) measured from the surface can adjust ATT for expected hydrometeor-atmosphere interactions. Applying calibrated cloud base height ATTs or a linear RH function for Norway (Sweden) reduced to 4.3% (2.8%) and 14.6% (8.9%) misclassified precipitation, respectively. Cloud base height ATTs had lower miss-rates with low cloud bases, 100 m in Norway and 300 m in Sweden. Combining the RH method with cloud base ATT in low cloud conditions resulted in 16.1 and 10.8% reduction in misclassified precipitation in Norway and Sweden, respectively. Therefore, the conceptual model output should improve through the addition of available surface data without coupling to an atmospheric model.

scholarly journals Sea Ice–Ocean Feedbacks in the Antarctic Shelf Seas

2019 ◽  
Vol 49 (9) ◽  
pp. 2423-2446 ◽  
Author(s):  
R. C. Frew ◽  
D. L. Feltham ◽  
P. R. Holland ◽  
A. A. Petty
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Mixed Layer ◽  
Surface Air Temperature ◽  
Weddell Sea ◽  
Surface Energy Budget ◽  
Atmospheric Conditions ◽  
Ocean Mixed Layer ◽  
Amundsen Sea ◽  
The Antarctic

AbstractObserved changes in Antarctic sea ice are poorly understood, in part due to the complexity of its interactions with the atmosphere and ocean. A highly simplified, coupled sea ice–ocean mixed layer model has been developed to investigate the importance of sea ice–ocean feedbacks on the evolution of sea ice and the ocean mixed layer in two contrasting regions of the Antarctic continental shelf ocean: the Amundsen Sea, which has warm shelf waters, and the Weddell Sea, which has cold and saline shelf waters. Modeling studies where we deny the feedback response to surface air temperature perturbations show the importance of feedbacks on the mixed layer and ice cover in the Weddell Sea to be smaller than the sensitivity to surface atmospheric conditions. In the Amundsen Sea the effect of surface air temperature perturbations on the sea ice are opposed by changes in the entrainment of warm deep waters into the mixed layer. The net impact depends on the relative balance between changes in sea ice growth driven by surface perturbations and basal-driven melting. The changes in the entrainment of warm water in the Amundsen Sea were found to have a much larger impact on the ice volume than perturbations in the surface energy budget. This creates a net negative ice albedo feedback in the Amundsen Sea, reversing the sign of this typically positive feedback mechanism.

scholarly journals Testing the applicability of physiographic classification methods toward improving precipitation phase determination in conceptual models

2020 ◽  
Vol 51 (2) ◽  
pp. 169-179
Author(s):  
Laurie D. Grigg ◽  
James Feiccabrino ◽  
Frederick Sherenco
Keyword(s):  
Meteorological Data ◽  
Surface Air Temperature ◽  
Classification Methods ◽  
Phase Determination ◽  
Alpine Regions ◽  
Temperature Thresholds ◽  
Maximum Elevation ◽  
Future Landscape ◽  
Precipitation Phase ◽  
Precipitation Events

Abstract Regions with a large percentage of precipitation occurring near freezing experience high percentages (>10%) of misclassified precipitation events (rain versus snow) and necessitate efforts to improve precipitation phase determination schemes through the use of more accurate surface air temperature thresholds (Trs). Meteorological data from 169 sites in Scandinavia were used to test the applicability of using physiographic categories to determine Trs. Three classification methods involving varying degrees of automation were evaluated. The two automated methods tested did not perform as well as when tested on a smaller region, showing only 0.16% and 0.20% reduction in error. A semi-manual method produced the largest average reduction in misclassified precipitation (0.53%) across all sites. Further refinement of classification criteria for mountain and hill stations showed that at mesoscales (>5 km), maximum elevation is a better predictor of Trs (0.89% average reduction in error) than terrain relief (0.22%), but that relief becomes increasingly important at microscales (0.90%). A new method for categorizing mountainous stations based on upslope or downslope air movement increased the average reduction in error up to 0.53%. These results provide a framework for future landscape classification methods and confirm the importance of microscale topography for determining Trs in alpine regions.

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