(重要)(英)利用向下红外辐射确定云量.doc

Algorithm for determination of cloud cover from downwellinginfrared radiation for use as part of an integrated automatic weather stationM J MolyneuxMet Office Beaufort Park Easthampstead Wokingham Berks RG40 3DN United KingdomTel +44(0)1344 85 5803 Fax +44(0)1344 85 5897 E-mail: AbstractThe Met Office has developed an automatic meteorological observing system known as SAMOS. When no observers are available, SAMOS is required to output values of total cloud cover. At the moment this is derived from Laser Cloud Base Recorder (LCBR) data with a method known as the Exponential Decay Algorithm. This is similar to the more widely used SMHI Larsson algorithm. The LCBRs used have been the Belfort 7013C and the recently introduced Vaisala CT25K. Early generations of LCBR suffered from problems during precipitation which degrades the cloud cover output, but whatever the quality of LCBR used, this method of determining cloud cover has limits. For example, it can only use the cloud information that has passed through its single measurement point. This makes it necessary to consider the introduction of other methods of measuring of cloud cover. However, these should not be seen as stand-alone systems but an additional input to a supervisory “cloud arbiter” software module which will be capable of improving the output. The arbiter concept is already in use in SAMOS - It is used to increase the quality of Present Weather output. Most complex instruments have features in their operation that have been well-established in trials and these can be improved by an arbiter. For example, if an instrument is known to give false alarms at low visibility and low temperatures - the arbiter can filter out the false alarms since it can monitor both these parameters.The work below describes an additional method of measuring cloud cover using downwelling infrared radiation from a pyrgeometer. This paper will be presented as a poster at TECO 2002.Description of the dataThe data from Camborne Principal Radiation Station (PRS) data was analysed for 2000. (Camborne is in Cornwall, Lat=50.133 degrees, Long=-5.100 degrees). For each hour the figure used is the average from minutes 51 to 60, which was thought to give the best correspondence to the human observer looking at the sky at observation time. These were merged into an Excel spreadsheet with full human observations using a template and a macro. The PRS has two pyrgeometers - labelled one and two - both are Eppley Precision Infrared Radiometers. The raw data used was uncorrected irradiance from pyrgeometer two in Wm-2, referred to as downwelling long wave (dwlw). AnalysisProcessing initially followed that suggested in the “Clear Sky Index” method (Marty 2000). This is based on detailed physics and modelling. However, it quickly became clear the equation used was not removing all the baseline variation of the signal. This may be because Marty uses data from Swiss sites at relatively high altitude. It is suggested that the lower altitude and frequent changes in air mass at Camborne have a large effect. Since the first attempt at processing was not successful, other investigations were made. Figure 1 shows a simple plot of the dwlw data against time for the whole year, two features are immediately clear. Firstly, there is a broad band of high frequency variability that should to be related to the amount of cloud. Secondly, there is a longer-term variability that is approximately seasonal. Tests were therefore carried out to determine if the baseline could be made flat and how well the short-term variability is correlated with cloud cover. Following Marty, the first parameters checked were screen temperature and dewpoint. This quickly revealed that the dwlw signal seasonality could be largely removed with a simple correction for dewpoint. This method is comparatively crude but it does seem to give results of sufficient quality. To demonstrate the relationship between dwlw and dewpoint Figure 2 was plotted. It shows dwlw against dewpoint for observations of 0&1 and 7&8 oktas of cloud - clear and overcast occasions as reported by the human observer. There is a good relationship, but there may be some non-linearity. It appears that there may be a “knuckle” at around 5 C dewpoint with linear sections above and below, although this may not be entirely clear due to the small size of figure 2. It doesnt quite look like a curve. The correction used applies 2 straight-line fits, one above and one below a 5C dewpoint.The corrections were applied to the full timeseries and the results are shown in figure 3 - the seasonal signal is no longer visible. This is a good result; it seems easy to remove the first problem with the data by using the dewpoint - a readily available measurement. Figure 4 shows the range of values of adjusted dwlw that were measured for occasions of 0 and 8 oktas. There are now clear groups of values of adjusted down welling long wave (adwlw) that show little sign of correlation with dewpoint.There is still some scatter in the results. Investigations were carried out to find any further correlations that might be used to sharpen the relationship. Correlations were sought with air temperature, wind speed and direction (for air mass), pressure, visibility, cloud height and if the sensor was wet (present weather). None of these tests gave a clear improvement and the results are not presented. For the time being a degree of scatter will have to be accepted. However, the cases that showed a large error during overcast conditions were plotted against time of day. This made it clear that most of the worst cases occurred in darkness. This may be a problem with the sensing method, but is just as likely to reflect the problems that the observers suffer in darkness.Using adwlw it is possible to identify clear and overcast conditions reliably. However there is more uncertainty when there are intermediate amounts of cloud.Radiometer signal level and variability may be used to identify cloud type (Duchon 1999). Although type identification is not attempted, it does suggest another parameter to examine for cloud cover information. The standard deviations (SD) for each preceding hour were used. Theoretically, most of the 0 and 8 okta reports will have very low SD since the signal should not vary much. Other amounts of cloud cover should have more variability. A further plot - figure 5 - shows the total number of occasions in each category of SD for 0&1, 8&9 and 2-7 oktas. This shows well that for the lowest SD it is most likely that there will be 0&1 or 8&9 oktas, for SD level 2 and above it is much more likely that there will be 2-7 oktas. Discussion and ConclusionsIt is shown that Infrared radiometer signal level and variability, corrected by screen dewpoint can give useful information on total cloud cover. The information is most reliable at high and low oktas of cloud cover, but this is aimed at improving the overall cloud cover. The choice of the best measurement will be carried out by arbiter software. As long as the arbiter can weight the measurements appropriately, there should be an improvement in output quality. The next phases of the work will involve pr