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Use of daily outgoing longwaveradiation (OLR) data in detectingprecipitation extremes in the tropicsSukumaran Sandeep a & Frode Stordal aa Department of Geosciences, Section for Meteorology andOceanography , University of Oslo , Oslo , 0315 , NorwayPublished online: 06 Mar 2013.

To cite this article: Sukumaran Sandeep & Frode Stordal (2013) Use of daily outgoing longwaveradiation (OLR) data in detecting precipitation extremes in the tropics, Remote Sensing Letters,4:6, 570-578, DOI: 10.1080/2150704X.2013.769284

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Remote Sensing Letters, 2013Vol. 4, No. 6, 570–578, http://dx.doi.org/10.1080/2150704X.2013.769284

Use of daily outgoing longwave radiation (OLR) data in detectingprecipitation extremes in the tropics

SUKUMARAN SANDEEP∗ and FRODE STORDALDepartment of Geosciences, Section for Meteorology and Oceanography, University

of Oslo, Oslo 0315, Norway

(Received 9 October 2012; in final form 18 January 2013)

Changes in precipitation extremes are getting attention in the context of a warm-ing climate. However, the lack of high-quality observations hinders the detectionof variability in daily precipitation extremes over several regions, notably over theoceans. The outgoing longwave radiation (OLR) observed by satellites have longbeen used as a proxy to detect deep convection over the tropics. Here, we proposea heavy precipitation index based on daily OLR data over the global tropics. Thenew OLR-based heavy precipitation index is validated using a corresponding dailyheavy precipitation index derived from the Tropical Rainfall Measuring Mission(TRMM) as well as the Global Precipitation Climatology Project (GPCP). We sug-gest that daily OLR can be used to further explore variability and trends in dailyprecipitation extremes over the tropics and to validate precipitation extreme indicesderived from model simulations.

1. Introduction

The global hydrological cycle is undergoing changes in an anthropogenic warming ofthe climate, with increased chances of wet and dry extremes over different parts of theworld (Solomon et al. 2007, Seneviratne et al. 2012). As both dry and wet extremes canhave profound impact on human beings and other living organisms (Easterling 2000),a better understanding of the regional-scale variability in the precipitation extremesis very important for devising mitigation and adaptation strategies. However, long-term continuous precipitation observations are lacking over several parts of the world,especially over Africa and South America (Frich et al. 2002) and over oceans. Almostall existing studies on precipitation extremes hitherto have been conducted over landregions, and the variability in daily precipitation extremes over the vast oceans arerelatively unknown. With the aid of satellite-borne sensors, daily observations overtropical oceans and land are available only since around 1997, and this short timeperiod hinders any meaningful long-term analysis of precipitation variability usingsatellite observations. Nevertheless, it is important to investigate the changes in pre-cipitation extremes over oceans, as they will affect atmosphere–ocean interactions andmay affect the oceanic ecosystem as well.

The daily outgoing longwave radiation (OLR) data measured by satellite-borne sen-sors have long been used as a proxy for detecting tropical deep convection (Waliser and

*Corresponding author. Email: sandeep.sukumaran@geo.uio.no

© 2013 Taylor & Francis

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Graham 1993, Zhang 1993, Krishnan et al. 2000, Matsumoto and Murakami 2000).Other applications of OLR data include developing an index for onset dates of rainfallover the Central Amazon region (Garcia and Kayano 2009, 2013) and investigation ofthe seasonal march of the monsoons between the northern and southern hemispheres(Matsumoto and Murakami 2002). Further, the monthly OLR data were successfullyused to develop an OLR-based precipitation index (OPI) (Xie and Arkin 1998). Oftenextreme precipitation in the tropics results from deep convective systems. This moti-vated us to investigate the possibility of using daily OLR data to characterize dailyprecipitation extremes over the global tropics. The main objective of this letter is tosuggest and validate an index which serves as a proxy for daily heavy precipitation,derived from daily OLR data, as an alternative to heavy precipitation indices already inuse derived from daily precipitation observations. This new OLR-based proxy for dailyheavy precipitation provides valuable information for long-term (∼3 decades) analy-sis of daily precipitation extremes over the vast oceanic and other conventional datasparse regions. Also, this OLR-based heavy rainfall index may be useful in validatingthe heavy precipitation variability simulated by the climate models. It is already knownthat the climate models often tend to underestimate the precipitation variability in awarming climate (Allan and Soden 2008).

2. Data and methodology

The daily interpolated OLR data with a resolution of 2.5◦ × 2.5◦ (latitude × longitude)from National Oceanographic and Atmospheric Administration (NOAA) polar orbit-ing satellites (Liebmann and Smith 1996) during the 1997–2011 period are used in thisstudy. It may be noted that, although the daily OLR data are available since 1974, theentire data during March–December 1978 are missing. As stated in the Introductionsection, the main objective of this letter is to derive an extreme precipitation index fromOLR and compare it with a much-used index derived from observed daily precipita-tion. Hence, we confine our analysis to the 1997–2011 period for which a completespatial coverage of daily precipitation data is available over the entire tropics. Therewere considerable drifts in the equatorial crossing times of the NOAA satellites (Bateset al. 1996), which are taken into consideration while preparing the daily OLR data.However, there are chances of spurious variability of data over land due to the driftsin satellite equatorial crossing times. While there are cases of missing data (Liebmannand Smith 1996) during the pre-1990 period, there are very few (<10%) missing datain the recent two decades. It has previously been found that OLR ≤ 220 W m−2 isassociated with deep convection over the tropics (Zhang 1993, Krishnan et al. 2000).As we are interested in heavy precipitation events, we use a stronger criterion, namelyOLR ≤ 180 W m−2, to detect even deeper convection that could cause extreme pre-cipitation. The number of days with OLR ≤ 180 W m−2 (OLR180) is counted foreach of the four seasons over each grid point. The seasons are December–February(DJF), March–May (MAM), June–August (JJA) and September–November (SON).The DJF season for 1998 consists of December 1997, January and February 1998.The number of days with 10 mm or more precipitation (R10) is a widely used index toanalyse daily precipitation extremes (Frich et al. 2002, Alexander et al. 2006). Here, wederived R10 from the sixth version of Tropical Rainfall Measuring Mission (TRMM)level 3 (3B42) (Huffman et al. 2007) and Global Precipitation Climatology Project(GPCP) version 1.1 (v1.1) (Huffman et al. 2001) daily precipitation data for four sea-sons in the same manner as the OLR180 days are counted. It is to be noted that daily

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precipitation from TRMM 3B42 is in fact derived from the three hourly precipitationestimates. The threshold of 180 W m−2 is selected after a careful comparison of severalthreshold values of OLR (≤220 W m−2) with R10 by calculating spatial correlationsbetween the OLR-based index and R10.

In order to examine inhomogeneities arising from the satellite drift as well asfrom missing data, the OLR-based extreme precipitation index was subject to ahomogeneity test (Wijngaard et al. 2003). The Wijngaard homogeneity test consistsof a suite of four independent homogeneity tests and if none or only one of thetests reject the null hypothesis (‘data is homogeneous’) at 95% level of significance,the data at that grid point is considered as ‘homogeneous’. If more than one testrejects the null hypothesis at 95% level, then the data at that grid point is consid-ered as ‘inhomogeneous’. It may be noted that in the standard Wijngaard test, thedata are classified into three categories – useful, doubtful and suspicious – based onthe test results, where ‘useful’ corresponds to our ‘homogeneous’. The homogeneitytest was carried out during 1979–2010 period, to increase the data sample. The resultsof the Wijngaard homogeneity test on the OLR180 revealed that 1%, 1%, 3% and 2%data points are inhomogeneous for the DJF, MAM, JJA and SON seasons, respec-tively, when all the data points are considered and no inhomogeneities when only wetgrid cells (R10 > 2) are considered. This provides us confidence in the quality andusefulness of the OLR data for long-term analysis of precipitation extremes.

While GPCP v1.1 data have a resolution of 1◦ × 1◦, the TRMM data have aresolution of 0.25◦ × 0.25◦. The R10 data derived from TRMM and GPCP dailyprecipitation are regridded to the resolution of OLR for the purpose of spatial com-parison. In order to quantitatively assess the relationship between OLR180 and R10 interms of spatial pattern and magnitude, we calculated the spatial correlation betweenthe two indices for each season, and this is repeated for all the years between 1997 and2011. The spatial correlations are calculated for the entire tropical belt (30◦ S–30◦ N).It may be noted that only those grid cells with R10 > 2 are considered for thecalculations of spatial correlations, in order to avoid dry regions. The statistical signif-icance of the spatial correlation values are calculated using a two-tailed t-test (Wilks2011). The El Nin̈o-Southern Oscillation (ENSO) is the main natural variability in thetropics which can affect the precipitation patterns globally through tele-connections(Ropelewski and Halpert 1987). We regressed OLR180 and R10 from TRMM dur-ing 1998–2011 with the seasonal Nino3.4 index (Trenberth 1997) obtained fromNational Oceanographic and Atmospheric Administration (NOAA) (http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml) to exam-ine whether the two indices respond to ENSO in the same manner.

3. Results and discussion

The 1998–2011 climatology of R10 days from TRMM and OLR180 days fromNOAA daily OLR are presented in figures 1(a)–(d). The colour shading representsR10, whereas OLR180 is depicted by contours. The spatial patterns of R10 andOLR180 are closely matching. Although the selection of threshold value of 180 W m−2

for OLR180 may seem somewhat arbitrary, the magnitude of OLR180 turns out to besimilar to R10. In the DJF season, the heavy precipitation days are mostly locatedsouth of the equator, with the exception of the Pacific warm pool region where theextreme precipitation days are distributed over both sides of the Equator (figure 1(a)).The southern tropical land regions of South America and Africa are found to have

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Figure 1. 1998–2011 climatology of R10 from TRMM (shading) and OLR180 from NOAAdaily OLR (contour) for (a) DJF, (b) MAM, (c) JJA and (d) SON. The contours range from 7 to35 with an interval of 7 days.

heavy precipitation days, with the former having higher numbers of R10 and OLR180.Further, the pattern of R10 and OLR180 resembles the Intertropical ConvergenceZone (ITCZ), which is not surprising as the ITCZ is associated with deep convec-tion and often heavy rainfall. The seasonal march of heavy precipitation connectedto the ITCZ is clear in figure 1(b), which depicts R10 and OLR180 for the MAMseason, when the heavy precipitation is mostly concentrated around the Equator.The prominent feature of the JJA season is the southeast Asian monsoon, the pat-tern of which is well captured by both R10 and OLR180 (figure 1(c)). The WestAfrican monsoon pattern is also captured by both heavy precipitation indices. Thesouthward march of the heavy precipitation days can be seen in the SON season(figure 1(d)), when the pattern of spatial distribution is somewhat similar to that ofMAM, with lesser magnitude over southern hemispheric land regions. A repetitionof the analysis for R10 days derived from GPCP daily precipitation yielded identicalresults (not shown). Our results are consistent with an earlier study that OLR (andapparently OLR180) is sensitive to the seasonal migration of the monsoons between

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northern and southern hemispheres (Matsumoto and Murakami 2002). The spatialcorrelation between the number of R10 and OLR180 days calculated over the entiretropical belt (30◦ S–30◦ N) for each of the four seasons during the 1998–2011 period isshown as time series in figure 2(a). High values of the correlation ranging from 0.65 to0.80 between R10 and OLR180 are found. The lowest correlation coefficient of about0.65 occurs during 2007 in the JJA season. Also, it may be noted that the JJA seasonhas lower correlation values compared with the other three seasons. This could be dueto the measurement of uncertainty of precipitation and/or OLR over the heavy mon-soon precipitation regions of the southeast Asian land mass. However, the correlationsbetween the two indices are still robust, as a t-test revealed that the correlation valuesfor all the years are statistically significant at 99%. Figure 2(b) is similar to figure 2(a),except that the spatial correlations are between GPCP-derived R10 and OLR180 dur-ing the 1997–2009 period. It may be noted that v1.1 of GPCP daily precipitation endsin August 2009. The correlation values between GPCP R10 and OLR180 are slightlyhigher than those between TRMM R10 and OLR180.

The linear regression coefficients of R10 on the Nino3.4 index for four seasons dur-ing 1998–2011 are depicted in the left-column panels of figure 3, whereas the samefor OLR180 are illustrated in the right-column panels. The regression slopes that arestatistically significant at 95% level, as revealed by a t-test, are stippled. The centralequatorial Pacific, which is known to receive more precipitation during warm ENSOevents, shows a positive regression pattern while the tropical warm pool region showsa negative pattern during the DJF season for both R10 (figure 3(a)) and OLR180(figure 3(b)). While the positive regression slope region of the eastern tropical Pacifichas a qualitatively similar pattern and magnitude in both R10 and OLR180, the warmpool over the Indo-Pacific region shows a strong negative slope and a more spatiallycoherent pattern in OLR180. The same is true for the MAM season (figures 3(c)and (d)) as well, with a more prominent negative slope over the Indo-Pacific regionfor R10 when compared with the DJF season. The regression slopes are found to beweaker during the JJA season for both R10 and OLR180 as seen in figures 3(e) and (f ),respectively. This may be expected as the ENSO achieves its maximum strength duringDecember and weakens thereafter. Also, it may be noted that the statistically signif-icant slopes of R10 and OLR180 have closely comparable patterns during JJA. Thepatterns of R10 and OLR180 regression slopes are rather weaker in SON (figures 3(g)and (h)) when compared with other seasons. A close examination of figure 3 revealssome differences between the regression slopes of R10 and OLR180. A weak contrast

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Figure 3. Linear regression slopes of seasonal Nino3.4 index against R10 from TRMM (left-column panel) and OLR180 (right-column panel) during the 1998–2011 period. The stipplingshows the regression slopes that are statistically significant at 95% level, as revealed by a t-test.(a) DJF TRMM R10, (b) DJF OLR180, (c) MAM TRMM R10, (d) MAM OLR180, (e) JJATRMM R10, (f ) JJA OLR180, (g) SON TRMM R10 and (h) SON OLR180.

in the regression patterns of R10 and OLR180 can be seen over equatorial region ofwestern South America in JJA and SON seasons. Another anomaly in the regressionpatterns is that OLR180 has a stronger and significant (p < 0.05) regression slopeover West African monsoon region in MAM and JJA seasons. Although it is difficultto pin point the reasons for these differences in the present analysis, the observationaluncertainty in either or both of the variables (precipitation and OLR) over land regioncould not be ruled out. Hence, the results over land regions, where contrasting regres-sion patterns are seen, need to be interpreted with care. However, it is to be notedthat the contrasting regression patterns in R10 and OLR180 are generally not statis-tically significant, except over West African monsoon region. The regression analysisof Nino3.4 on GPCP R10 also yielded similar results, but are not shown here forbrevity. Our results suggest that, in general, OLR180 is more sensitive to ENSO whencompared with R10.

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4. Conclusions

The possibility of using daily OLR data to explore extreme precipitation variabilityglobally over the tropical region is investigated. It is found that the number of dayswith OLR ≤ 180 W m−2 is closely associated with the number of days with precipi-tation ≥ 10 mm. R10 and OLR180 show a strong and significant (p < 0.01) spatialcorrelation during all the four seasons during the 1998–2011 period. Among R10sderived from GPCP and TRMM, the former is found to be strongly correlated withOLR180. The two indices have similar response patterns to ENSO as revealed bythe regression slopes of R10 and OLR180 days with the Nino3.4 index. At the sametime, the contrasting regression patterns over some land regions suggest that the OLR-based results over land regions, especially those over West Africa and South Americaneed to be interpreted with care. Whereas only a little more than one decade of pre-cipitation data are available for analysis of extreme precipitation over data sparseregions, more than three decades of daily OLR data can be utilized to understandregional-scale variability and trends in daily precipitation extremes in the tropics.This is essential for local planning of disaster preparedness strategies. The plans forfollow-up work include a comparison of the observed OLR180 with those computedfrom the twentieth-century simulations of different climate models participating in thefifth-generation coupled model inter-comparison project (Taylor et al. 2011).

AcknowledgementsThe authors thank Dr Timothy Warner and two anonymous referees for their helpto improve a previous version of this manuscript. This work was supported by theSoCoCA project funded by the FRIMUF programme of the Research Council ofNorway.

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