From db87c3f01bf3607807f74cf5a759d8db55426340 Mon Sep 17 00:00:00 2001 From: Bewa Date: Tue, 30 Jul 2024 14:23:56 +0300 Subject: [PATCH] Add files via upload --- ..._Comparing_Data_from_Different_Sources.qmd | 172 +++++++++--------- 1 file changed, 83 insertions(+), 89 deletions(-) diff --git a/Chapter_10_Comparing_Data_from_Different_Sources.qmd b/Chapter_10_Comparing_Data_from_Different_Sources.qmd index 2b64b7a..0945b4f 100644 --- a/Chapter_10_Comparing_Data_from_Different_Sources.qmd +++ b/Chapter_10_Comparing_Data_from_Different_Sources.qmd @@ -1,90 +1,84 @@ -# Comparing Data from Different Sources -## Introduction - -The satellite and reanalysis data, discussed in Chapter 9, provides a -wonderful resource that can supplement the historical station data that -is described in this guide. The satellite data is usually from the early -1980s, while some of the reanalysis data is from 1950. Table 10.1 -summarises some sources of rainfall data: - - ----------------------------------------------------------------------- - Table 10.1 - ----------------------------------------------------------------------- - ![](media/image115.png){width="6.268055555555556in" - height="1.9694444444444446in"} - - ----------------------------------------------------------------------- - -Some of these products also include other elements, including -temperatures and ERA5 is for many elements. - -These data are already used extensively. However, often users access -only one type, i.e. either station or satellite/reanalysis. This is -often either because that is what the researcher is comfortable with, or -only one type is easily available. We consider here how station and -satellite data can be compared and then perhaps used together. There are -a range of possible objectives from these comparisons including the -following: - -a) The satellite/reanalysis data, from the same location as a ground - station, can perhaps be considered as an additional station. As - such, perhaps the data can be used to complete, or infill missing - values in the station data. - -b) Similarly, perhaps this new (satellite) station could be used to - support the quality-control of the station data. - -These objectives may be more interesting for countries where there is a -relatively sparse station network. Where the network is dense, -neighbouring ground stations may be used for these objectives. - -c) The bonus is that the satellite data does provide a dense network. - For example, for CHIRPS the estimated daily rainfall data is on - roughly a 5km square, so the equivalent of about 400 (pseudo) - stations per square degree. Hence it provides estimated daily - rainfall data for the whole of Africa, and beyond with a pseudo - station that is always close to any given location. - -Comparisons between station and gridded data must recognise that they -have not measured the same thing. Station data are measured at a point, -while gridded data represent an area. The size of the area depends on -the method with an example shown in Fig. 10.1a. - -In Barbados, Fig. 10.1a the point shown is a station called Husbands, -the site of a regional climate centre. CIMH. The largest pixel is for -the ERA5 reanalysis data and the smallest is for CHIRPS. This figure -also shows that the pixel in coastal sites can sometimes be largely over -the ocean and hence a neighbouring pixel may be more relevant. - -+------------------------------------+---------------------------------+ -| ***Fig. 10.1a Pixel size for 3 | ***Fig. 10.1b Difference | -| methods in Barbados*** | between gridded and point data | -| | for rainfall*** | -| | | -| | (Figure with permission from H. | -| | Greatrex) | -+====================================+=================================+ -| ![Chart Description automatically | ![](media/image104.pn | -| generated](media/image11 | g){width="2.7712182852143483in" | -| 2.png){width="3.094673009623797in" | height="2.506793525809274in"} | -| height="2.188830927384077in"} | | -+------------------------------------+---------------------------------+ - -Fig 10.1b illustrates a reason for possible differences between area and -point data for rainfall. The sketch shows a cloud, and hence possibly -rain in part of the pixel, but not at the station in the top left. Hence -the station may be zero, while the gridded data notes some rain. Thus, -unless the satellite data are adjusted, we would expect more rain days -(and potentially less extreme values) than at a point. This feature is -particularly for rainfall, but may also be shown for other elements, -such as sunshine hours, where there may be zeros in the data. - -The problem that is addressed in this chapter is essentially just the -comparison of two variables, i.e. 2 columns of data, where the first is -the station and the second is the satellite, or reanalysis data. This is -essentially the same problem as in forecasting, where the forecast is -compared with the actual data. Many of the methods are from software -that was originally constructed for the forecasting problem. - -From a statistical point of view this problem is just the same as +# Comparing Data from Different Sources +## Introduction + +The satellite and reanalysis data, discussed in Chapter 9, provides a +wonderful resource that can supplement the historical station data that +is described in this guide. The satellite data is usually from the early +1980s, while some of the reanalysis data is from 1950. Table 10.1 +summarises some sources of rainfall data: + + ----------------------------------------------------------------------- + Table 10.1 + ----------------------------------------------------------------------- + ![](figures/Table10.1.png){width="6.268055555555556in" + height="1.9694444444444446in"} + + ----------------------------------------------------------------------- + +Some of these products also include other elements, including +temperatures and ERA5 is for many elements. + +These data are already used extensively. However, often users access +only one type, i.e. either station or satellite/reanalysis. This is +often either because that is what the researcher is comfortable with, or +only one type is easily available. We consider here how station and +satellite data can be compared and then perhaps used together. There are +a range of possible objectives from these comparisons including the +following: + +a) The satellite/reanalysis data, from the same location as a ground + station, can perhaps be considered as an additional station. As + such, perhaps the data can be used to complete, or infill missing + values in the station data. + +b) Similarly, perhaps this new (satellite) station could be used to + support the quality-control of the station data. + +These objectives may be more interesting for countries where there is a +relatively sparse station network. Where the network is dense, +neighbouring ground stations may be used for these objectives. + +c) The bonus is that the satellite data does provide a dense network. + For example, for CHIRPS the estimated daily rainfall data is on + roughly a 5km square, so the equivalent of about 400 (pseudo) + stations per square degree. Hence it provides estimated daily + rainfall data for the whole of Africa, and beyond with a pseudo + station that is always close to any given location. + +Comparisons between station and gridded data must recognise that they +have not measured the same thing. Station data are measured at a point, +while gridded data represent an area. The size of the area depends on +the method with an example shown in Fig. 10.1a. + +In Barbados, Fig. 10.1a the point shown is a station called Husbands, +the site of a regional climate centre. CIMH. The largest pixel is for +the ERA5 reanalysis data and the smallest is for CHIRPS. This figure +also shows that the pixel in coastal sites can sometimes be largely over +the ocean and hence a neighbouring pixel may be more relevant. + + ------------------------------------------------------------------------------------------------------------- + ***Fig. 10.1a Pixel size for 3 methods in Barbados*** ***Fig. 10.1b Difference between gridded and point data for rainfall*** + ------------------------------------------------------ ------------------------------------------------------ + ![](figures/Fig10.1a.png){width="3.094673009623797in" ![](figures/Fig10.1b.png){width="2.7712182852143483in" + height="2.188830927384077in"} height="2.506793525809274in"} + + ------------------------------------------------------------------------------------------------------------- + +Fig 10.1b illustrates a reason for possible differences between area and +point data for rainfall. The sketch shows a cloud, and hence possibly +rain in part of the pixel, but not at the station in the top left. Hence +the station may be zero, while the gridded data notes some rain. Thus, +unless the satellite data are adjusted, we would expect more rain days +(and potentially less extreme values) than at a point. This feature is +particularly for rainfall, but may also be shown for other elements, +such as sunshine hours, where there may be zeros in the data. + +The problem that is addressed in this chapter is essentially just the +comparison of two variables, i.e. 2 columns of data, where the first is +the station and the second is the satellite, or reanalysis data. This is +essentially the same problem as in forecasting, where the forecast is +compared with the actual data. Many of the methods are from software +that was originally constructed for the forecasting problem. + +From a statistical point of view this problem is just the same as comparing \ No newline at end of file