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This is a preview of subscription content, log in to check access. Alter JC Shielded storage precipitation gauges. Barry RG Mountain weather and climate. Plateaus flat areas of high elevation can also have an affect on precipitation. Thermal lows can form over plateaus in the subtropics like Mexico or parts of Asia , causing precipitation.
This is a form of a summer monsoon. Elevation affects temperature which affects the amount of rain. Most raingauges around the world are within a few hundred metres of sea-level, i. Within this range the rainfall-elevation relationship often appears to be linear.
However, as the temperature gets cooler with altitude, the maximum precipitable moisture decreases drastically, so the rainfall-altitude curve reflexes back upon itself, so that there is an elevation of maximum precipitation 'EMP'. This variation variies with several factors, such as continentality, 'tropicality', aspect, rain shadow and barrier effect.
In the case of polar regions the EMP is ground level because the rainfall-altitude relationship is nearly always negative. Above the EMP the rainfall decreases rapidly with altitude, which is why high mountainous regions are essentially deserts. Wherever there is significant relief in a catchment, the first step is to determine whether the catchment is all below the EMP.
The second step is to process the historic record to see if a second order, or even third order rainfall altitude regression is a significant improvement upon 'linear'. If you want further refinement the seasonal rainfalls, or rainfalls from different directions, often have markedly differing rainfall-altitude relationships.
If you want to go the whole hog, and be scientific about it, then the rainfall approximates a multi-variate solution, with components of altitude, altitude squared, aspect, regional trend, rain shadow and barrier effect. Areas of high elevation, such as mountain ranges, often drain the air of its moisture. As the air rises up the mountain, it cools.
As the air cools, it loses its ability to hold water. The water then condenses out of the air and falls as precipitation. The high altitudes of mountains often receive a significant amount of precipitation. Avoid altitude sickness on high elevation ski trips by hydrating. Newsroom Snow Science Does elevation affect temperature? It sure does. Wondering why? On-Snow example Temperature change formula Check out these related articles.
Related Stories. Snow science: How mountains make snow November 01, This study demonstrates that the orographic enhancement of precipitation dependence on anthropogenic forcing is an important research line, and more observational data and modeling work are necessary in order to quantify the effects of both aerosols and global warming on it, which will allow us to make more reliable predictions of precipitation and water storage in the mountain regions in the coming decades.
It will also be important to assess whether the distribution of precipitations and extreme conditions such as heavy rains, consecutive wet days, consecutive dry days present long term variability that systematically depends on elevation. In this work two different precipitation datasets have been used. The first one is a high density monthly dataset over Italy and the Alpine region for the period — To this aim, many different datasets have been included. Starting in early s, many mechanical rain gauges have been dismissed and replaced with automatic instruments.
In most cases, this has been done without maintaining the old and the new instruments operative at the same time, to calibrate the new instruments, and for this reasons the time series cannot be merged to generate longer time series.
Moreover, precipitation data have been collected at the national level for several decades and the operations have been regionalized at the end of the 20 th century.
For these reasons, the period — has been chosen to guarantee a degree of homogeneity of data availability. Careful quality checks and data filling have been performed At the end of this procedure, the dataset provides monthly precipitation at every station for the 30 year long period with no gaps. The number of available stations in the area considered in this paper is A second dataset is comprised of historical time series of monthly precipitation for stations in the Lombardy region.
This dataset has been created at the Department of Environmental Science and Policy of University of Milan by analyzing and digitizing a large amount of historical data archives monographic studies, bulletins, reports, etc. In a second step available time series have been homogenized to remove non-climatic changes in the data: in the last decade, the scientific community has become aware of the fact that the real climate signal in original series of meteorological data is generally hidden behind non-climatic noise caused by station relocation, changes in instruments and instrument screens, changes in observation times, observers, and observing regulations, algorithms for the calculation of means and so on.
So, at present, the statement that time series of meteorological data cannot be used for climate research without a clear knowledge about the state of the data in terms of homogeneity has a very large consent Different stations have data available for different time periods and some data between the starting and end times of each station are missing.
So a preliminary treatment of the dataset has been necessary. For each station, a climatological monthly precipitation was computed. We then considered, for each station, only years in which at least 9 months of data are present and we replaced the missing ones by the corresponding climatological value.
We then computed the annual and the seasonal precipitation at each station by summing over the considered months. At the end of the procedure we obtained a dataset of precipitation with a variable number of stations every year. In the Supplementary Fig. In this work, the focus has been on the period from onward in order to have a relatively stable statistics. Each of those time series has been normalized, either on its own mean value of annual precipitation and on an estimate of the mean annual precipitation appropriate for its elevation, as described below.
A model of the elevation dependent annual mean precipitation has been constructed: using the high density database for the — period, we have divided the data in 10 classes based on station altitude, where the class limits are chosen so that each class has the same number of stations The annual mean precipitation for each station has been computed and then the mean value of the annual mean precipitation over the stations in the same altitude class has been computed, together with its standard deviation, and its standard error.
The results of this procedure are shown in Fig. A linear interpolation between nearby classes has been performed to define the annual mean precipitation at each elevation between the central value of each class. This model has then been used to normalize the precipitation data from the Lombardy region. To this aim, the model annual mean precipitation corresponding to the elevation of each Lombardy station has been calculated.
The annual precipitation of each station in the Lombardy region has then been divided by these model values. We refer to this dataset as the Lombardy region normalized dataset. To verify that results are not sensitive to the details of the normalization procedure, we also used as normalizing factor the mean precipitation of the individual Lombardy station.
The results are similar and all the figures presented in the paper refer to the former normalization method. This allows to form two classes with nearly equal number of stations see Supplementary Fig. We varied the precise value of the elevation threshold used to define mountain and lowland stations to verify that the results do not show a strong sensitivity.
A time series has been constructed for each of the two classes, taking for each year the average of the normalized annual precipitation over all the available station in each class. The ratio between the resulting dimensional time series is the Orographic Enhancement of Precipitation Index, as shown in Fig. Annual and seasonal indices have been obtained by averaging the monthly index over the appropriate months.
Positive and negative NAO phases have been defined as values of the index whose distance from the mean is larger than the standard deviation. The correlation between detrended variables has been calculated through the Pearson correlation coefficient which measures their linear dependence.
The datasets generated and analysed during the current study are available from the authors on reasonable request. Viviroli, D. Mountains of the world, water towers for humanity: Typology, mapping, and global significance. Water resources research 43 Beniston, M. Regional behavior of minimum temperatures in switzerland for the period — Giorgi, F.
Elevation dependency of the surface climate change signal: a model study. Fyfe, J. Enhanced climate change and its detection over the rocky mountains.
Gao, X. Projected changes in mean and extreme precipitation over the mediterranean region from a high resolution double nested rcm simulation. Siler, N. How will orographic precipitation respond to surface warming? Pepin, N. Elevation-dependent warming in mountain regions of the world. Johnson, G. Topographic and atmospheric influences on precipitation variability over a mountainous watershed. Frei, C. A precipitation climatology of the alps from high-resolution rain-gauge observations.
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