MODULE 4.3A SHORT-RANGE FORECASTING OF WEATHER ELEMENTS. Temperature

Transcription

1 MODULE 4.3A SHORT-RANGE FORECASTING OF WEATHER ELEMENTS Temperature

2 Table of Contents TABLE OF CONTENTS 1 1. INTRODUCTION 2 2. SITE/PROCESS APPROACH: 2 3. TRAJECTORY/PROCESS APPROACH: 2 4. PATTERN/PROCESS APPROACH: 2 5. SHORT RANGE TECHNIQUES: 2 a) Convective Temperature 2 b) Minimum Temperature 3 c) Empirical Relationships 4 d) Tephigram Overlay for Predicting Maximum Temperature 4 e) Approximation of the rate of thermal advection 8 6. FROST (SCREEN MINIMUM TEMPERATURE VS. GRASS MINIMUM TEMPERATURE) 8 REFERENCES 9 Meteorologist Operational Internship Program 1

3 1. Introduction Many short range temperature forecasting techniques have been developed over the years and they encompass quite a number of approaches to the task. The discussions which follow are by no means exhaustive but they do represent a survey of techniques that have been used successfully. Three approaches can be taken to forecasting the temperature at a site and although the approaches are different they can incorporate many of the same specific techniques. 2. Site/Process Approach: With this method one starts with the current conditions at the station and then all significant processes which are expected to modify this temperature are approximated using specific techniques. These processes may include advection, insolation, radiation, evaporation, etc. Consult the temperature diagnosis document for a detailed discussion of processes. This is an Eulerian approach. 3. Trajectory/Process Approach: In this case the trajectory of a parcel which would be at the station at a forecast time is mapped. Then the significant processes expected to act on the parcel during the forecast period are approximated using specific techniques. This is a Lagrangian approach. 4. Pattern/Process Approach: Here the first step is to establish the relationship between the station and the synoptic pattern (including thickness value) at forecast time. Secondly identify a representative temperature fitting the same thickness and synoptic pattern at T 0 (at the corresponding diurnal analysis time) using the established relationship. The third step is to allow for any different physical processes which will modify this temperature during the forecast period. The pattern/process approach is the most popular technique operationally since it is readily visualized. 5. Short Range Techniques: a) Convective Temperature A quick method for determining today's maximum temperature (for use in the late morning update forecast) is to calculate the Convective Condensation Level (CCL) and then the convective temperature (T C ). This is a quick assessment and will require adjustment on the basis of other parameters especially thermal advection. Meteorologist Operational Internship Program 2

4 If the sounding is too dry to calculate T C one can make a rough guess by bringing down a dry adiabat from 850 mb to the surface (see Figure 1). In the case of Figure 1, the maximum will be degrees warmer than the 850 mb. This is a valuable method since 850 mb forecast temperatures are readily available from NWP. Figure 1. Finding T max from a 12Z sounding using a dry adiabat from 850 mb. There are some important constraints to note when using this technique. Firstly the area between the two curves represents the energy provided by solar heating. Early in the year, say April, there will be less insolation so that a colder dry adiabat must be used. Of course cloud cover, albedo, and surface wetness will affect the value. The Kawaga method, with a tephigram overlay and which accounts for cloud cover and albedo, will be presented later in this document. Any advections should be taken into account if you are using a morning sounding rather than the 850 mb forecast temperature. Even so, you must be sure that a dry adiabatic lapse rate will form, otherwise the maximum temperature will be overestimated by several degrees, especially during the cooler seasons. b) Minimum Temperature For minimum temperatures, in a case of little advection, there are two techniques. The first is to forecast the same minimum temperature as the previous night. This method works well in situations where none of the factors which might affect minimum temperature have changed from the previous night. In such cases, spatial variability can be quite high, especially in hilly country where the temperature difference between hilltop and hollow can be several degrees. The second technique is to forecast the minimum temperature 2 to 5 degrees colder than the current airmass dew point. It may be a good indication of the minimum temperature Meteorologist Operational Internship Program 3

5 since, in order to fall below this temperature considerable latent heat of condensation would be returned to the lower atmosphere. Under clear skies and light winds, the dewpoint temperature will fall by some 2 to 5 degrees, depending on temperature, relative humidity and stability near the surface. In the case of strong advection, the best method is the pattern/process approach using thickness correlations and cloud amounts. c) Empirical Relationships Many empirical equations have been developed for individual stations. Given a large sample of historical data at each station, one can develop a simple equation which predicts the minimum temperature based on 2 P.M. temperatures and subtracting the mean diurnal temperature change for the season in question. These equations can also include observed wet bulb temperatures and even wind speed. Obviously, these would work well only under synoptic patterns which show little change. e.g. T min = T(20Z) - at w (20Z) - b + cv a, b, c - constants (determined from climate data) T w - wet bulb T - dry bulb V - wind speed d) Tephigram Overlay for Predicting Maximum Temperature Since area on a tephigram is directly proportional to energy, it is reasonable to use this in predicting maximum temperatures where the major change in surface temperature is likely to be due to insolation. (Figure 2, Kagawa, 1968). Meteorologist Operational Internship Program 4

6 Figure 2. Nomogram used to predict maximum temperature. The nomogram is used in the following way: 1. Choose a representative sounding for the desired location. 2. Align line AC on the nomogram with the surface pressure of the sounding. 3. Calculate the energy flux (in cal/cm 2 ) available for the heating (See Table 1). Find the corresponding value on the line A-B. For example triangle A - B200 - C200 - A represents a net flux of 200 cal/cm 2 into the layer near the surface. 4. Slide the nomogram horizontally until the environment curve equalizes areas on either side of the line ABXXX, where XXX represents the flux calculated earlier. 5. Read off the maximum temperature at CXXX. Figures 3 and 4 show two applications. Meteorologist Operational Internship Program 5

7 Figure 3. Figure 4. Meteorologist Operational Internship Program 6

8 TABLE 1. Energy Available for Heating the Lower Atmosphere Maximum Potential Insolation (cal/cm 2 ) - clear skies MAR APR MAY JUNE JULY AUG SEPT OCT NOV 45 N N N N A. Albedo of cloud cover and atmosphere will reduce above values significantly Cloud Amount % Reduction Cloud Amount % Reduction B. Also the albedo of the surface reduces the effective energy available for heating of the air. Surface % Reduction Cultivated Soil & Vegetation 8 Bare Ground 14 Grass 20 Dry Sand 18 Wet Sand 9 Fresh Snow 85 Old Snow 55 Reduce remaining amount by a further 20% to account for heating of the soil. D. Reduce the energy available again to account for long wave radiation from the surface between sunrise and maximum temperature time (See Table 2). e.g. With an average temperature of 21.1 C between sunrise and maximum temperature time, and 10 hours duration, 128 cal/cm 2 would be lost due to terrestrial radiation. Note: evaporation of water is not considered. Meteorologist Operational Internship Program 7

9 TABLE 2. TEPHIGRAM OVERLAY - T max FORECASTING Step D: Long Wave Radiation HOURS BETWEEN SUNRISE & MAX TEMP AVG TEMP C Values found in Table are Net Loss of Long Wave Terrestrial Radiation in cal/cm 2 (S 0 ) for clear skies and for various lengths of time (Based on Hewson & Longley s Formula). S 0 = 2.85 x T 4 cal/cm 2 min -1 ENERGY LOST = S 0 (l-kn) Here k = and n =.03 for Cirrus.06 for middle Cloud.09 for low Cloud.10 for Nimbostratus Average Cloud Amount in tenths e) Approximation of the rate of thermal advection From David A- Gustin, National Weather Service Forecast Office, Washington, D.C. The surface advection during the night equals 0.5 of warm advection at 850 mb or 0.7 of cold advection at 850 mb. 6. Frost (Screen Minimum Temperature vs. Grass Minimum Temperature) The thermometers located in the Stevenson screen are approximately 1.5 metres above the ground. The grass minimum thermometer is located just above short grass (8 cm high). The grass minimum thermometer is useful in situations where the confirmation of frost is of interest. Often there can be a large difference between minimum temperatures reported at screen level versus those at grass level. These temperature differences are largest under clear skies and with light winds and are a reflection of the strong radiation inversion established in low levels. Meteorologist Operational Internship Program 8

10 7. References 1. Dickey, W.W., 1960: Forecasting Maximum and Minimum Temperatures. Forecasting Guide No. 4. U.S. Department of Commerce. 2. Kagawa, N.H., 1968: Design and Evaluation of a Tephigram Overlay for Predicting Maximum Temperatures. Technical Memoranda TEC Petterssen, 1940: Weather Analysis and Forecasting. McGraw-Hill Book Co. Meteorologist Operational Internship Program 9