Estimating Daily Tmin and Tmax
from Thermal Infrared Satellite Data
Lizzie Good
© Crown copyright Met Office
Contents
This presentation covers the following areas
• Motivation and EURO4M deliverable
• Satellite vs in situ surface temperatures
• Satellite air temperature estimates
• Method
• Results
• Some things to think about…
• Conclusions
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Study motivation
• Gridded station data
widely used but:
• Some grid cells are poorly
represented or absent due
to low station density /
lack of stations.
• There is an increasing
need for higher spatial
resolution from the user
community. Station data
cannot offer fine spatial
detail (e.g. urban).
Can satellite data help improve
coverage and spatial
representation?
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Density of Global Historical Climate
Network-Daily (GHCN-D) Stations
Image Source: National Climatic Data Center,
http://www.ncdc.noaa.gov/oa/climate/ghcn-daily/
EURO4M D1.11
• MO will generate maximum and minimum surface air
temperature products from satellite data available in
the Land-SAF. These will be made available along
side the gridded air temperature datasets based on
surface observations. These efforts will result in a
blended satellite/in situ dataset, which will aid
heatwave analysis, etc. In addition, sunshine hours
will be estimated. The results will be used in WP3.
This talk:
satellite max and min surface temperature data
http://www.metoffice.gov.uk/hadobs/msg_tmaxmin/
http://www.metoffice.gov.uk/hadobs/msg_sunshine/
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Satellite vs in situ temperatures
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LST vs Near Surface Air Temp (1)
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Diurnal cycle observed by SEVIRI and in situ station
60735 (35 40N, 10 06E – Tunisia)
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Satellites observe land
surface temperature (LST)
LSTs not ‘measured’
Satellites observe ‘skin’
temperature not air → large
differences
LSTs dependent on view
angle/direction (e.g looking at
one side of a mountain)
Cloud a problem for infrared
observations = no surface
view
Microwave obs through cloud,
but not precip.
●
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Polar orbiting only, pixel
size tens of km.
LST vs Tair (2)
• Difference between satellite LST
(SEVIRI/MSG) and temporally
and spatially collocated HadISD
station temperatures (Dunn et
al., 2012) over Europe between
0 and 20 degrees longitude for
each month during 2010
Users interested in Tair
so need to ‘correct’
satellite LST data to air
temps
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Satellite air temperature estimates
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Sensor choice: SEVIRI (MSG)
• METEOrological SATellite Second
Generation (MSG)
• Spinning Enhanced Visible and
InfraRed Imager – from 2002
• Spatial resolution: 1-3 km, sub-satellite
• Temporal resolution = 15 minutes
• Channels: 12 (Vis, NIR, IR)
• Split-window IR channels accuracy:
radiometric accuracy ~ 0.3 K
DATA USED HERE:
Image: NCAR
(http://www.rap.ucar.edu/~djoh
nson/satellite/)
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• Eumetsat Land Surface Analysis
Satellite Applications Facility (LSA SAF)
• Land Surface Temperature (LST)
• Fraction of Vegetation Cover (FVC)
Methods
Europe,
2012-2013
Relationship between skin and air temperature complicated.
Depends on several factors (geography, surface
characteristics, elevation, solar heating, meteorology)
Existing satellite obs → Tair approaches:
• Empirical model (nearly all papers): multiple linear regression
using station data and various predictors, e.g. veg index (NDVI),
latitude, etc
• Thermodynamic model (Sun et al. (2005)), Tair calculated from
LST, NDVI, net radiation, aerodynamic resistance and crop
water stress index (CWSI).
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15-minute LST data (LSA-SAF)
Public station Tmin/Tmax
over Europe (ECA&D)
Daily FVC data (LSA-SAF)
Analysis of LST over temporal
window
Fraction of cloud-free
observations in window
Estimated LSTmin and
LSTmax for each pixel.
LST uncertainty (LSASAF) at LSTmin/max
observation time
Regression analysis of station
data with satellite data to
derive relationship over 11-day
moving window
Ancillary Data (LSTSAF; GlobCover-derived)
Daily regression
coefficients
Non-public station Tmin/Tmax
for Gemany and UK (ECA&D)
Validation of satellite Tmin
and Tmax using independent
station data.
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Regression coefficients applied
to central day in 11-day
window for every pixel
Estimated Tmin and
Tmax for each pixel
Process of
deriving
minimum and
maximum land
temperatures.
Input data sets
are in green
and output data
sets are in
yellow
Station Data: ECA&D
• ECA&D stations
(publicly
available) with
Tmin/Tmax data
for 2012/2013.
European Climate
Assessment and
Dataset (ECA&D;
Klein Tank et al.,
2002)
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Regression
• Regressing observed station Tmin/Tmax data against
temporally and spatially collocated:
•
•
•
•
•
•
LSTmin/LSTmax
fraction of green vegetation (FVC)
latitude (Lat)
elevation (Z)
urban fraction (UF)
distance from coast (DfC)
• Moving 11-day window
for coefficient calculation.
LSTmin/LSTmax:
estimated from analysing 15minute images of SEVIRI LST
data between 23:00-08:00
(LSTmin) and 11:00-16:00
(LSTmax)
UF/DfC:
ESA 300m GlobCover land use
map
T min = α min + β min .LST min +χ min .FVC + δ min .Z + φmin .UF + ϕmin .Lat + γ min .DfC
T max = α max + β max .LST max +χ max .FVC + δ max .Z + φmax .UF + ϕmax .Lat + γ max .DfC
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Tmin
(a) number of cloud-free
satellite-station matchups
on day of observation (i.e.
central day in window),
(b) correlation coefficient
between station air
temperature and
collocated predictor
variables (black line
indicates multiple linear
regression coefficient),
(c) normalised multiple linear
regression coefficients,
(d) T-values for coefficients,
(e) regression coefficients,
(f) regression offset, and
(g) residuals (i.e. satellite
minus station air
temperatures) on day of
observation (i.e. central
day in window), the solid
black line indicates the
median.
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2012/2013
Tmax
(a) number of cloud-free
satellite-station matchups
on day of observation (i.e.
central day in window),
(b) correlation coefficient
between station air
temperature and
collocated predictor
variables (black line
indicates multiple linear
regression coefficient),
(c) normalised multiple linear
regression coefficients,
(d) T-values for coefficients,
(e) regression coefficients,
(f) regression offset, and
(g) residuals (i.e. satellite
minus station air
temperatures) on day of
observation (i.e. central
day in window), the solid
black line indicates the
median.
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2012/2013
∆Tmin
Magnitude of ∆Tmin in
2012/2013 with respect to
(a), (b) minimum/maximum
correction for elevation
(c), (d) minimum/maximum
correction for distance from
coast and
(e), (f) minimum urban fraction
correction and
Values are calculated by
applying the minimum and
maximum values of the
regression coefficients for
these variables to each pixel in
the static data set.
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∆Tmax
Magnitude of ∆Tmin in
2012/2013 with respect to
(a), (b) minimum/maximum
correction for elevation
(c), (d) minimum/maximum
correction for distance from
coast and
(e), (f) minimum urban fraction
correction and
Values are calculated by
applying the minimum and
maximum values of the
regression coefficients for
these variables to each pixel in
the static data set.
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Products (21 August 2013)
LSTmin
Tmin
Tmin
LSTmax
Tmax
Tmax
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Tmin
Non public ECA&D stations only
Validation: Germany +
UK for 2012-2013
(a) the number of
satellite-station
matchups,
(b) the correlation
between station Tmin
and LSTmin, and
station Tmin and
satellite Tmin,
LSTmin minus station
(c) satellite LSTmin
minus station Tmin
(d) satellite Tmin minus
station Tmin
distributions for each
day.
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SEVIRI Tmin minus station
Tmax
Non public ECA&D stations only
Validation: Germany +
UK for 2012-2013
(a) the number of
satellite-station
matchups,
(b) the correlation
between station Tmin
and LSTmin, and
station Tmin and
satellite Tmin,
LSTmax minus station
(c) satellite LSTmin
minus station Tmin
(d) satellite Tmin minus
station Tmin
distributions for each
day.
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SEVIRI Tmax minus station
Some things to think about…
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Satellite vs in situ results
• Satellite Tmin/Tmax data are likely to be more accurate
than results suggest. Inherrent differences between
satellite and in situ because:
• In situ is point, satellite is areal average
• Spatio-observational discrepancies from parallax effects
(interaction between the SEVIRI line of sight/view angle and
topography).
• Large pool of data → reduce any effects on the mean
bias but not the variance, which is likely to be inflated as
a result of the above.
• Performance of Satellite LSAT estimates in this study
similar to other studies.
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The cloud problem
• Undetected cloud = errors
in LSTs (cold bias)
• LSTmax: less of a problem
since max LST is least likely
to be cloudy.
• LSTmin: big problem since
this is most likely to be
cloudy
• Cloud explain why satellite
Tmax is better than Tmin?
• Clouds lead to missing
data – more a problem
during the day and at high
latitudes
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Percentage of days in 2010 with
cloud-free SEVIRI observations at
synoptic times (labelled on plots).
Percentage of days in 2010 with cloud-free observations at
HadISD station locations (Dunn et al., 2012) plotted as a function
of (a) latitude and (b) date and coloured according to observation
time.
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Conclusions
External webpage
• An empirical regression-based method has been developed to
estimate air temperatures from satellite data.
• Could potentially be applied to any satellite LST data set
• For most days >50% of the estimated LSATs are within 3 °C of
collocated station observations, with around 80% within 4 °C and 90%
within 5 °C.
• Results for Tmax are better than for Tmin.
• The mean bias of the satellite-estimated LSATs oscillates around zero
and shows no seasonal variation, although the variance is noted to be
slightly lower during summer months (cloud more easily detected?).
• Evaluation results may be worst-case scenario as the variance will
be inflated through the inherent, random discrepancies that arise from
comparing satellite area-averaged with in situ point temperature
observations
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Acknowledgements
• The SEVIRI data were provided by the Land Surface
Analysis Satellite Applications Facility (LSA SAF)
• Daily tmin/tmax station data were provided by ECA&D
(Klein Tank et al. 2002; http://www.ecad.eu).
• Six-hourly station data were sourced from HadISD (Dunn
et al., 2012; http://www.metoffice.gov.uk/hadobs/hadisd/)
• We also acknowledge the ESA and ESA GlobCover
project, led by MEDIAS-France, for the provision of
GlobCover data.
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Questions and answers
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