In the Troposphere, the Temperature Decreases with Altitude
Indian Journal of Geo-Marine Sciences Vol. 44(7), July 2015, pp. 1071-1095
Study of TRMM estimated freezing level height in the 36N – 36S region
Rajasri Sen Jaiswal *, Sonia R Fredrick, M Rasheed, V S Neela & Leena Zaveri
Centre for Study on Rainfall and Radio wave Propagation, Sona College of Technology, Salem-636005, India
[E-mail: [email protected] ]
Received 21 April 2014; revised 14 October 2014
In this paper the freezing level height (HFL), a very important parameter in cloud physics has been studied in the latitudinal belt 36N-36S, during the period 1999-2002 and 2007-2008. Present study shows that the latitude is the major controlling factor of the HFL, rather than LH or temperature. Further, it is seen that the HFL depends on season. The study also reveals that the HFL responds in a different manner over the continental and the oceanic sites. Functional relationship between the HFL and latitude shows seasonal dependence. It also presents the range of the HFL, its maximum and minimum values and the months of occurrence of the maxima/minima over few selected locations, including India.
[Keywords: Freezing level height, latitude, temperature, latent heat]
Introduction
In the troposphere, the temperature decreases percentage of liquid precipitation8. This is shown with altitude. The height at which the temperature for both mid latitude and Arctic region. Freezing falls to 0°C is known as 0°C isotherm height, or level height is a very important parameter in freezing level1. The freezing level during rainy climatology. During the precipitation process, condition is known as the rain height. In general, when the falling frozen water drops encounter the the 0°C isotherm height is considered to be the freezing level, just below the freezing level, they transition height between the liquid particles form a coating of water, and form what is called below and the frozen particles above. However, as “snow flake”. Water, having higher dielectric this simplified picture is found to be valid in constant than snow, reflects the radar beam more stratiform precipitation alone. In convective than the latter, thereby producing a bright band9. precipitation, because of strong updraft, there is The estimation of the bright band, and, in large scale mixing of different particle types2. turn, the freezing level, remains of major concern Thus, super cooled liquid drops may be present to the communication engineers as the bright above the 0°C isotherm height. Recent studies band causes an enhancement of reflectivity of 5- show that super cooled liquid drops are present 10 dB, and thus the estimated rainfall rate can be above the transition height in several events of 5 times higher than the actual value10. It has been stratiform precipitation also3. found that the variation in freezing level affects HFL has been found to show diurnal snow cover, glaciers and permafrost11. The variations. It shows daily, monthly and yearly freezing level height is found to affect the altitude variations, too4. The freezing level height is the of the glacier7. Thus, the study of the freezing highest in the tropics, and it decreases pole ward. level is of immense importance in climatology, The variation in the freezing level height, hydrology and in communication. however, is less over the equator, and it increases The variations of the HFL from one location pole ward5. Maximum variation of about 400 m is to another, and its temporal variations have found at mid latitudes. formed the motivation behind the present study. The HFL is reported to increase at a rate of The rain height model as suggested by ITU-R12 is 13.4 m per decade over Antofagasta6. A study unable to predict correct picture of the parameter shows that the HFL has increased over the over India. It appears that the ITU-R model12 is tropics, especially over the outer tropics7. valid for neutral atmosphere13. For atmosphere As microwave radiation of snow and rain is where lot of convective activity prevails, as in the different, the microwave retrieval algorithms tropical atmosphere, the ITU-R model12 is not require the freezing level height as the input5. The appropriate; a new model must be sought for. freezing level height also determines the phase of Moreover, the ITU-R model12 does not take precipitation that reaches the Earth’s surface. A seasons into account. Thus, it gives the same lower freezing level height ensures greater values of HFL in all months and years over a percentage of solid precipitation on ground, while place. In addition to this, the ITU-R model12 gives a higher freezing level height increases the 1072 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015 the same values of HFL for all locations below The HFL values are the heights of the 0°C 23°. isotherm above the mean sea level in meters14, Present study aims to give a model for the and are obtained from the data product 2A2315 of HFL, which is universally valid. In this paper, the PR. Height of the freezing level is attempt has been made in particular, to find out estimated16-17 from the knowledge of the the controlling factors of the HFL. In this context, climatological surface temperature data17 the variations of the HFL with latitude and some provided by the National Aeronautics and Space of the meteorological parameters, viz. latent heat Administration (NASA) and the temperature (LH), surface temperature and height of a location lapse rate. As the HFL values are estimated using from the mean sea level have been studied. the climatological surface temperature data, it Functional relationships between the HFL and the needs improvement for better reliability over above parameters have been established, if any. land. The dependence of HFL on LH has been studied The TMI generates hydrometeor profiles on on oceanic sites alone as the LH data derived a pixel by pixel basis. For each pixel, the latent from the Tropical Microwave Imager (TMI) heat (LH) values are given at 14 vertical levels14, onboard the TRMM are valid over ocean only14. and are stored in the data product 2A1215. These LH values are multiplied by 10, and stored as a 2- Materials and Methods byte integer15. LH values are recorded in °C/hr. It In this paper the authors aim to study the is noteworthy that the TMI generates hydrometeor HFL within the latitudinal belt of 36 N – 36 S. profiles during rainy conditions alone (personal The data required for the study are the HFL communication with the TRMM team). The 2A23 values measured by the precipitation radar (PR) contains flags describing whether the data have and the latent heat (LH) values measured by the been recorded over the continent, ocean or over microwave imager (TMI) onboard the tropical the coast. Status flags 0, 10, 20, 30, 50 and 100 rainfall measuring mission (TRMM) satellite for represent data taken over the ocean14, while the the period 1999-2002, 2007 and 2008. The data status flags 1, 11, 21, 31, 51 and 101 represent are in Hierarchical Data Format, and are data over the continent14. Hence, while selecting converted to ASCII before further analyses. oceanic and continental locations, the status flags Surface temperature data in °C and the height of of the data product 2A23 of the TMI have been the locations from the mean sea level in metre are considered. In addition to this, whether the obtained from the radiosonde flights of the locations chosen are continental or oceanic were National Oceanic and Atmospheric also verified from the world atlas. The details of Administration (NOAA). the oceanic and continental locations included in this study are described in Table 1.
Table 1 — List of stations studied
Latitude (degree) Longitude (degree) Location Geography
-33.56 18.28 Cape Town Land, Ocean -32 24 Cape Province Land -30.58 141.27 Broken Hill Land -28.3 29 Drakensberg Land -22.2 -49.5 Bauru Land -21.2 -41.2 Campos Land -20.27 -55.45 Aquidauana Land -17.8 38.18 Mozambique Ocean -17 -164 Pacific Ocean Ocean -16.33 35.1 Chiroma Land -15 -65 Bolivia Land -13 15 Angola Land -12.02 -77.02 Lima Land -11.15 -37.28 Estincia Land, Ocean -10 -77 Andes Mount Mountain -9.41 -85.2 Abuna Ocean -8.5 126 East Timor Land, Ocean SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1073
-6.02 -50.1 Caragas Land -5.23 -49.1 Maraba Land -4 147 Papua New Guinea Ocean -3.1 -60 Manaus Land -2 78 Indian Ocean Ocean -1.2 -48.3 Belem Land -1 16 Congo Land 0 90 Indian Ocean Ocean 1.17 103.51 Singapore Land, Ocean 2.35 113.3 Sarawak Land 3.11 101.4 Kuala Lumpur Land, Ocean 4 38.4 Rudeolf Land 5.3 73 Maldives Land, Ocean 6.56 79.56 Colombo Ocean 7.43 81.44 Batticaloa Land 8.29 76.59 Trivandrum Ocean 8.5 -79.5 Panama Land, Ocean 9.18 -85.43 Costa Rica Ocean 9.55 76.14 Kochi Land, Ocean 10.1 77.06 Munnar Land 10.33 72.39 Kavaratti Ocean 11.39 78.12 Salem Land 11.41 92.43 Port Blair Land, Ocean 11.43 79.49 Cuddalore Land 12.58 72.38 Bangalore Land 13.04 80.17 Chennai Land, Ocean 13.42 100.3 Bangkok Land, Ocean 14.41 77.39 Anantapur Land 15.3 73.55 Panjim Land, Ocean 16.09 81.12 Machilipatnam Land, Ocean 16.2 77.37 Raichur Land 16.31 80.39 Vijaywada Land 17.42 83.5 Vishakhapatnam Land, Ocean 18.55 72.54 Mumbai Land, Ocean 18.97 113.4 South China Sea Ocean 20.15 85.52 Bhubaneshwar Land 22.34 88.29 Calcutta Land 22.9 -94.2 Gulf of Mexico Ocean 23.03 72.4 Ahmadabad Land 23.23 85.23 Ranchi Land 23.3 87.2 Durgapur Land 23.67 87.72 Bolpur Land 24.41 85.02 Bodh Gaya Land 24.44 93.58 Imphal Land 25 121 Taiwan Land, Ocean 25.17 91.47 Cherrapunji Land 25.34 91.56 Shillong Land 25.67 94.12 Kohima Land 26.09 91.5 Dispur Land 1074 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015
26.12 91.42 Guwhati Land 26.32 88.46 Jalpaiguri Land 26.55 75.52 Jaipur Land 28.38 77.12 New Delhi Land 30 123 East China Sea Ocean 30.42 76.54 Chandigarh Land 35.67 12.12 Mediterranean Sea Ocean
The TMI generated hydrometeor profiles are In order to find out as to which parameter (s) valid over ocean only14. Hence, while studying contributes (contribute) to the height of the the dependence of the HFL on LH, only oceanic freezing level, the parameters have been included sites have been chosen. However, while studying in the regression analysis one by one, and the one the dependence of HFL on surface temperature (s) which explains (explain) HFL more has (have) and latitude, locations have been chosen been suggested as the controlling factor of the irrespective of land and ocean. Both surface HFL. temperature and LH data were available over 12 In order to investigate the latitudinal locations, namely, Mozambique, Papua New variations of the longitudinally averaged HFL, the Guinea, the Indian Ocean, Trivandrum, Panama, daily values of HFL have been obtained at each Costa Rica, Chennai, Panjim, Machilipatnam, latitude, in intervals of 2°, in the latitudinal belt Vishakhapatnam, Mumbai and Taiwan. Hence, 36N-36S for all longitudes 180E-180W, and then the dependence of HFL on LH and temperature the average over all the longitudes is estimated. has been investigated over these 12 locations Next, these daily longitudinally averaged values alone. Moreover, over these 12 locations, the at a particular latitude, are averaged over a month. dependence of HFL on latitude and elevation has This is how the monthly averages are found out. also been investigated. Dependence of HFL on Next, the monthly averages of the longitudinally latitude has also been studied including larger averaged values at each of the latitudes and the number of oceanic and continental locations corresponding latitudes have been fitted to separately as described in Table 1. The different models, viz. s, logarithmic, logistic, dependence of HFL on latitude has been cubic, linear, quadratic, power, exponential, investigated by joining oceanic and continental growth, inverse and compound. The validity of latitudes also. It is noteworthy that over Chennai, the model is judged by F test at 5% level of Port Blair, Kochi, Vishakhapatnam, Mumbai, significance. Machilipatnam, Panjim, Bangkok, Panama, In order to find out the yearly correlations Maldives, Kuala Lumpur, Singapore, East China between the HFL and the latitude, the yearly Sea, Estincia, Taiwan and Cape Town, the 2A23 averages are found out from the monthly averages status flag sometimes represents data taken over at each latitude and then these yearly averaged the ocean and sometimes over the continent. HFL values and the corresponding latitude values Thus, while selecting oceanic locations, the data are fitted to different models as described above. corresponding to ocean flags have been The study has also been performed over the considered and while selecting continental selected locations as mentioned in Table 1. For locations, the data corresponding to the land flags this purpose, the daily values of HFL are obtained have been considered over these locations. over a particular location. From the daily values, In order to establish the functional the monthly averages and yearly averages are relationship between the HFL and a particular found out. meteorological parameter, the two have been fitted to different models, viz. s, logarithmic, Results and Discussion logistic, cubic, linear, quadratic, power, Table 2 shows the statistics of HFL over a exponential, growth, inverse and compound. The few selected locations. Table 2 shows that the validity of the model is judged by F test at 5% HFL exhibits monthly variations. level of significance.
Table 2 — The statistics of HFL over the selected stations
Station Year Range HFL (min/max) (m) Occurrence month of min/ max Yearly avg. HFL (m) HFL
Costa Rica ‘99 4510 - 4790 Nov & Dec/May 4630 ‘00 4510 - 4790 Nov & Dec/Jun 4630 SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1075
‘01 4510 - 4790 Dec/May 4630 ‘02 4510 - 4790 Nov & Dec/May 4520 ‘07 4510 - 4790 Nov & Dec/May 4630 ‘08 4510 - 4790 Nov & Dec/May 4630 East China Sea ‘99 2108 - 4690 Feb/Aug 3470 ‘00 1950 - 4690 Feb/Aug & Sep 3430 ‘01 2030 - 4630 Feb/Aug 3410 ‘02 2090 - 4680 Feb/Aug 3460 ‘07 2140 - 4690 Feb/Aug 3340 ‘08 1860 - 4690 Feb/Aug 3510 Gulf of Mexico ‘99 3850 - 4860 Feb/Aug 4380 ‘00 3860 - 4860 Feb/ Sep 4370 ‘01 3860 - 4860 Feb/Aug 4360 ‘02 3860 - 4860 Feb/Aug 4380 ‘07 3830 - 4860 Feb/Aug 4320 ‘08 3860 - 4860 Feb/Aug 4420 Indian Ocean ‘99 4680 - 4860 Sep,Oct & Nov/Apr 4750 ‘00 4680- 4860 Oct/Apr 4750 ‘01 4680- 4850 Oct & Nov/Apr 4750 ‘02 4680- 4850 Oct & Nov/Apr 4750 ‘07 4680- 4860 Oct & Nov/Apr 4750 ‘08 4680- 4850 Oct & Nov/Apr 4740 Mediterrenean Sea ‘99 2520 - 4300 Feb/Aug 3290 ‘00 2510 - 4320 Feb/Aug & Sep 3280 ‘01 2510- 4280 Feb/Aug 3250 ‘02 2520- 4280 Feb/Aug 3160 ‘07 2500- 4290 Feb/Aug 3100 ‘08 2510– 4300 Feb/Aug 3380 Mozambique ‘99 3990 – 4730 Aug/Feb & Mar 4380 ‘00 3990 - 4740 Mar/Apr 4390 ‘01 3990 – 4730 Jul/Mar 4400 ‘02 3980 – 4730 Aug/Mar 4380 ‘07 4020 – 4740 Aug/Mar 4430 ‘08 3950 – 4730 Aug/Mar 4350 Pacific Ocean ‘99 4290 – 4710 Aug/Feb , Mar & Apr 4520 ‘00 4290– 4710 Aug/Feb , Mar & Apr 4530 ‘01 4300 – 4710 Aug/Feb , Mar & Apr 4540 ‘02 4290 – 4710 Aug/Feb , Mar & Apr 4520 ‘07 4290– 4730 Aug/ Mar 4360 ‘08 4220 – 4710 Sep/Feb , Mar & Apr 4430 Panama ‘99 4530 – 4700 Feb/May 4630 ‘00 4530 – 4690 Feb/Jun 4630 ‘01 4530 – 4700 Feb/May 4620 ‘02 4530 – 4690 Feb/Jun 4630 ‘07 4530 – 4710 Feb/Apr & May 4410 ‘08 4530 – 4710 Feb/May 4630 Papua New Guinea ‘99 4620 – 4860 Aug/Dec 4780 ‘00 4610 – 4860 Aug/ Nov & Dec 4780 ‘01 4620 – 4860 Sep/Nov 4780 ‘02 4610– 4860 Aug/Nov 4700 ‘07 4610– 4880 Sep/Nov 4790 ‘08 4600 – 4870 July/Jan 4770 South China Sea ‘99 3910 – 4840 Feb/Jun & Jul 4470 ‘00 3910 – 4840 Feb/Jun & Jul 4460 ‘01 3900 – 4840 Feb/Jun & Jul 4450 ‘02 3910 – 4840 Feb/Jun & Jul 4470 ‘07 3850 – 4840 Feb/Jun 4410 ‘08 3910 – 4840 Feb/Aug 4510 Taiwan ‘99 3210 – 4740 Feb/Aug 4050 ‘00 3210 – 4740 Feb/Aug & Sep 4030 ‘01 3210 – 4740 Feb/Aug 4040 ‘02 3210 – 4740 Feb/Aug 4050 ‘07 3140 – 4740 Feb/Aug 3970 ‘08 3150 – 4740 Feb/Aug 4070 Bangalore ‘99 4550 – 4940 Aug/Apr 4710 ‘00 4550 – 4940 Aug/Apr 4710 ‘01 4560 – 4940 Aug/May 4.72 ‘02 4540 – 4940 Aug/Apr 4710 ‘07 4540– 4940 Aug/May 4640 ‘08 4550 – 4950 Aug/Apr 4710 Calcutta ‘99 3940 – 4840 Jan/Jun 4500 ‘00 3940 – 4830 Jan/May 4500 1076 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015
‘01 3930 – 4840 Jan/Jun 4410 ‘02 3940 – 4830 Jan/Jun 4500 ‘07 3940 – 4840 Jan/Jun 4550 ‘08 3940 – 4840 Jan/Jun 4510 Chennai ‘99 4470 – 4950 Jan/May 4700 ‘00 4470 – 4950 Jan/May 4700 ‘01 4460 – 4950 Jan/May 4700 ‘02 4470 – 4950 Jan/May 4710 ‘07 4490 – 4950 Jan/May 4720 ‘08 4480 – 4950 Jan/May 4710 Guwahati ‘99 3240 – 4670 Feb/Sep 4090 ‘00 3260 – 4690 Feb/Oct 4080 ‘01 3260– 4680 Feb/Oct 4080 ‘02 3260– 4680 Feb/Aug 4060 ‘07 3290 – 4680 Feb/Sep 4070 ‘08 3180 – 4760 Feb/Jul 4070 Machilipatnam ‘99 4180 – 4970 Nov/May 4690 ‘00 4380 – 4960 Jan/May 4680 ‘01 4360 – 4970 Jan/May 4690 ‘02 4390 – 4960 Jan/May 4680 ‘07 4390 – 4960 Jan/May 4510 ‘08 4250 – 4970 Sep/May 4690 New Delhi ‘99 3320 - 4450 Feb/Jun 3940 ‘00 3320– 4420 Feb/Jun 3940 ‘01 3320– 4400 Feb/Jun 3940 ‘02 4330 – 4450 Feb/Jun 3970 ‘07 3320 – 4440 Feb/Jun 3900 ‘08 3320 – 4420 Feb/Jul 3940 Panjim ‘99 4560 – 4940 Aug/May 4710 ‘00 4560– 4940 Aug/May 4710 ‘01 4570 – 4940 Aug/May 4720 ‘02 4560– 4940 Aug/May 4710 ‘07 4560– 4940 Aug/May 4710 ‘08 4560– 4940 Aug/May 4710 Salem ‘99 4560– 4940 Aug/Apr 4710 ‘00 4560– 4940 Aug/Apr 4700 ‘01 4560– 4940 Jan/Apr 4700 ‘02 4.55 – 4940 Aug/Apr 4700 ‘07 4560– 4940 Jan/Apr 4620 ‘08 4570 – 4870 Aug/Nov 4700 Trivandrum ‘99 4580 – 4960 Aug/Apr 4720 ‘00 4580 – 4950 Aug/Apr 4720 ‘01 4590 – 4950 Aug/Apr 4730 ‘02 4580– 4950 Aug/Apr 4720 ‘07 4580– 4960 Aug/Apr 4720 ‘08 4580– 4950 Aug/Apr 4710 Vishakhapatnam ‘99 4330 – 4960 Jan/May 4680 ‘00 4330 - 4960 Jan/May 4670 ‘01 4330 - 4950 Jan/May 4670 ‘02 4330 - 4960 Jan/May 4690 ‘07 4330 - 4950 Jan/May 4700 ‘08 4300 – 4950 Jan/May 4680 Mumbai ‘99 4340 – 4890 Feb/May 4610 ‘00 4340 – 4890 Feb/May 4610 ‘01 4330 – 4880 Apr/May 4590 ‘02 4330 – 4890 Feb/May 4620 ‘07 4350 – 4880 Feb/May 4630 ‘08 4340 – 4880 Feb/May 4610
northern hemisphere, the maximum HFL values It is seen from Table 2 that the HFL attains are found to occur in the month of April/ May, i.e. its maximum and minimum values in the same in the NH summer, while the minimum HFL month in each year. At locations in the southern values are found to occur in December/January, hemisphere, the HFL attains its maximum values i.e. in the winter season. This can be explained as in the month of December and January, i.e. in the follows: In summer, the temperature over a SH summer. The minimum value is attained in location is high. It is well known that as altitude the month of May, i.e. in the southern increases, temperature decreases in the hemispheric winter. Over locations in the troposphere. Thus, the snow starts melting at SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1077 higher altitude, as compared to winter time when the temperature is low, thereby lowering the freezing level height. Table 2 further shows that the year 2007 has seen below average HFL over most of the places, except Calcutta where the below average HFL is found to occur in 2001 and Guwahati in 2002. Table 2 further shows that the same trend of monthly average freezing level height is observed in every year. Figure 1a and 1b respectively shows the monthly variations of freezing level height in
2000 and 1999 (Figures for other locations not shown).
1078 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015
Fig. 1a Variation of monthly average HFL in 2000 over a few Indian locations.
SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1079
Fig. 1b Variation of monthly average HFL in 1999 over a few global locations.
Same trend of monthly average freezing level height is observed in every year. Figure 2a shows the monthly average of the longitudinally averaged HFL values in intervals of 2° latitude during the period 1999-2002, 2007 and 2008.
1080 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015
Fig. 2a Variation of monthly average (longitudinally averaged) HFL with latitude.
Figure 2b shows the variation of longitudinally averaged HFL with latitude on 3 August 1999 (results for other years not shown).
Fig. 2b Variation of longitudinally averaged HFL with latitude on 3 August1999.
It is seen from Fig. 2a and 2b that the HFL varies with latitude. However, at low latitude (within 10N-10S), the variation is quite less. The same result is observed on all days of observation SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1081 during the period of study (Figures not shown). In location from mean sea level have been studied the region 10N-10S, there is not much spatial by entering each parameter in the regression one variation in the incoming solar radiation by one, and combination of any two/ three (insolation). Hence, the spatial variability in parameters in each regression (results not shown). temperature is also less in this region. Thus, HFL The study shows that the latitude alone explains does not vary much at low latitudes between 10N- the freezing level height the best, rather than the 10S. Harris Jr. et al5 have also reported that the LH or the temperature or any combinations of variability is lower in the tropics and larger in the these parameters. LH shows significant subtropics and in the higher latitudes. Figures 2a correlations with the HFL very rarely (refer and 2b further show that the HFL is the highest at appendix: Table 1) and the temperature does so in the equator, thereafter as the latitude increases in very few cases (refer appendix: Table 2a and 2b). both the hemispheres, the HFL gradually An attempt has been made to find the correlation decreases. The variation of the HFL with the between the HFL and surface temperature using latitude can be explained in the light of average radiosonde data. The data were available over insolation received at the Earth’s surface at each Bangalore and Trivandrum for the year 1998. The latitude. Average insolation, and in turn, the study shows that no correlations exist between the average temperature is the maximum at the two in any months, as shown by very poor R2 and equator19-20. As one approaches the poles, starting sig F values (refer appendix: Table 3). Elevation from the equator, the average insolation and the of the location above the mean sea level does not average temperature gradually decrease, attaining show any correlations with the HFL (refer the minimum values at the poles20. If the appendix: Table 4). temperature is high over a place, the ambient In order to find out the functional temperature is also high in the atmosphere. Thus, relationships between the HFL and the latitude, the 0°C isotherm height also occurs at a higher the study has been extended to larger number of level. On the other hand, if the surface oceanic and continental locations as described in temperature is lower, the ambient temperature in Table 1. It is found out that the HFL bears the atmosphere above is also low. Thus, the significant correlations with the latitude over the freezing level also comes down. The latitudinal continental locations in every month and the variations of HFL in different months in a year relationship is cubic always (results for individual show the same trend. months of all years not shown). The correlation Figures 2a and 2b show dips at 20N and 20S between the HFL and the latitude has also been over the oceanic sites. Subtropical high pressure investigated over the continental locations in belts lie within 20°- 40° in both the hemispheres, India. It is found out that the relationship is cubic most prominent over the oceans21, where always except in 3% cases when it is quadratic atmospheric stability prevails. High pressure, and (results for individual months of all years not in turn, atmospheric stability at these latitudes shown). ensures lower temperature in comparison to that The HFL shows very strong correlations with at lower latitudes, thereby producing dips at 20° the latitude over the oceans, as well; the in both the hemispheres, especially over the correlations are mostly cubic. In 10% cases it is oceans. At 20S, the sudden prominent dip in HFL quadratic and during January- March and occurs on almost each and every day in all December 2007- April 2008 and in December months and in all years (all figures not shown). 2008 no correlations are found out between the However, at 20N, the sudden prominent dip in HFL and the latitude over the oceans (results for HFL is seen on some days, and it is not that individual months of all years not shown). prominent always. It is noteworthy that the In order to find out whether the HFL southern hemisphere is mostly covered by oceans, responds to the continents and the oceans in a while the oceanic mass in the northern different manner, the functional relationships hemisphere is interrupted by continents. Thus, the between the monthly average HFL and the subtropical high pressure belts are consistent over latitude over the oceans and continental locations the southern hemisphere, while in the northern have been analysed. It is observed that the R2 hemisphere, the high pressure regions are values of the regression are higher over the inconsistent22. Thus, the fall in HFL at 20S is oceans as compared to those over the continents consistent throughout the year, while at 20N, during November-June, while in the months of though it is present, the dip is not that prominent August, September and October, the continents always. show better correlations mostly. In the month of In order to find out the parameters governing July, the correlations are sometimes stronger over the HFL, the correlations between the HFL and the oceans and sometimes stronger over the few meteorological parameters, viz. surface continents. It is noteworthy that in 2007 and 2008 temperature, LH, latitude and height of the the continents always show better correlations 1082 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015 than the oceans. The continents show significant correlations in all months. From this study it appears that the HFL responds to the continents and the oceans differently. Thus, it is justified to suggest separate functional relationships between the two over the oceans and the continents. The study has been repeated by including the continental and the oceanic latitudes together in the regression. It is found out that the HFL bears significant correlations in all months; the relationship is mostly cubic. It is further found out that no correlations are seen in January 2007 and in January-February 2008. In order to find out whether the monthly correlations between the HFL and the latitude are better over the yearly ones, the study has been repeated by fitting yearly average HFL values and Fig. 3b Variation of yearly average HFL with latitude in the latitudes of the locations in the regression 2002 over global continental locations. analysis. The yearly average HFL is found to bear significant correlations with the latitude over the But the monthly correlations are found to be Indian locations (Fig. 3a). The relationship is stronger than the yearly ones. The variation of the sometimes cubic and sometimes quadratic. yearly averaged HFL with latitude over the However, the monthly correlations are found to oceanic locations is shown in Fig. 3c. Over the be stronger than the yearly ones. The same study oceanic locations also, the monthly correlations has been repeated with continental locations are found to be stronger than the yearly ones. covering the entire globe as mentioned in Table 1.
Fig. 3c Variation of yearly average HFL with latitude in 2000 over oceanic locations. Fig. 3a Variation of yearly average HFL with latitude in 2000 over Indian locations. It is found out from Table 3a-3c that there exist The result is a very good correlation with a very little variations in the coefficients and cubic relationship (Fig. 3b). constants of the functional relationships from one month to another.
Table 3a — Functional relationships between HFL and latitude over the continental locations
Equation Month R2 Sig F Std error
Jan 0.705 0.000 276.132 Feb 0.781 0.000 250.468 Mar 0.782 0.000 246.152 Apr 0.755 0.000 256.207 SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1083
May 0.811 0.000 252.113 Jun 0.842 0.000 255.808 Jul 0.815 0.000 283.726 Aug 0.802 0.000 304.114 Sep 0.780 0.000 316.918 Oct 0.807 0.000 275.988 Nov 0.731 0.000 235.848 Dec 0.684 0.000 267.663
Table 3b — Functional relationships between HFL and latitude over the continental locations in India
Month R2 Sig F Std error Equation
Jan 0.917 0.000 117.444 Feb 0.963 0.000 108.547 Mar 0.948 0.000 128.141 Apr 0.933 0.000 102.871 May 0.915 0.000 77.400 Jun 0.926 0.000 45.222 Jul 0.884 0.000 56.748 Aug 0.829 0.000 78.023 Sep 0.836 0.000 70.137 Oct 0.938 0.000 45.166 Nov 0.922 0.000 73.786 Dec 0.954 0.000 87.339
Table 3c — Functional relationships between HFL and latitude over the oceanic sites
Month R2 Sig F Std error Equation
Jan 0.870 0.000 244.184 Feb 0.890 0.000 255.362 Mar 0.896 0.000 240.146 Apr 0.825 0.000 273.403 May 0.806 0.000 259.772 Jun 0.828 0.000 205.656 Jul 0.784 0.000 258.313 Aug 0.769 0.000 263.997 Sep 0.761 0.000 260.184 Oct 0.740 0.000 276.040 Nov 0.720 0.000 293.933 Dec 0.834 0.000 250.411
On the other hand, these values are almost R model12 does not include the seasonal effect on constant for the same month in all the years. the HFL. On the other hand, the model suggested Hence, separate functional relationships have in this paper gives different values in different been suggested for different months. Table 3a months as shown in Table 5 (refer appendix). It is shows the functional relationships between the seen that the model derived HFL values always HFL and the latitude over the continents. Table underestimate that derived from the ITU-R 3b shows the functional relationship over the model12. A comparison has been made between continental locations in India and the functional the HFL values derived from the model and that relationships over the oceanic locations have been obtained from the National Oceanic and mentioned in Table 3c. Atmospheric Administration (NOAA) over a few Unlike the ITU-R model12, the functional stations, where the HFL values from the latter relationships presented in this paper help to were available. The result is shown in Table 5 estimate the HFL values at all latitudes and in (refer appendix). It is found that the HFL values each month separately. According to the ITU-R estimated by the model mostly underestimated model12, the HFL value for Mozambique, the that derived from the NOAA. Pacific Ocean, Papua New Guinea, the Indian Ocean, Trivandrum, Panama, Costa Rica, Salem, Conclusion Bangalore, Chennai, Panjim, Machilipatnam, The latitude of a location appears to explain Vishakhapatnam, Mumbai, the South China Sea, the HFL most accurately and the two are strongly Calcutta and the Gulf of Mexico is 5km, the ITU- correlated with each other. The relationship is 1084 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015 cubic. HFL shows significant correlations over Geophys. Res. Lett., 36(2009) L17701, doi: the oceans, the continents and also combining 10.1029/2009GL037712. 8 Bradley, R.S., Equilibrium – line altitudes, mass oceanic and continental locations together. balance and July freezing level heights in the Correlations are stronger over the oceans in the Canadian high Arctic, J. Glaciol., 14(1975) 267- months of November-June, while in the months of 274. August-October, stronger over the continents 9 Zawadzki, I., Szyrmer, W., Bell, C. & Fabry, F., mostly. Thus, the HFL is found to show strong Modeling of the Melting Layer. Part III: the density effect, J. Atmos. Sci., 62(2005) 3705-3723. seasonal dependence. In 2007 and 2008 stronger 10 Joss J & Waldvogel A, Precipitation estimates and correlations are found over the continents always. vertical reflectivity profile corrections, paper It is noteworthy that 2007-2008 were La Nina presented at the 24th Conference on Radar years. Meteorology, Tallahassee, Florida, 1989. 11 Zhang, Y. & Guo, Y. Variability of atmospheric freezing level height and its impact on the Acknowledgement cryosphere in China, Ann. Glaciol., 52(2011) 81- Authors are grateful to the management of 88. Sona College of Technology, Salem for providing 12 Radiometeorological Data, ITU-R Report 563-4, the necessary infrastructure to carry out the work, Geneva, 1990. 13 Sen R, Seetaramayya P, Arulraj S, Kulkarni J R & Indian Space Research Organization (ISRO) for Singh M P, On the relationship between the financial support and the TRMM website and the surface meteorological elements and freezing level, National Oceanic and Atmospheric paper presented at the 10th National Space Science Administration (NOAA) for providing the Symposium, Ahmadabad, India, 1997. 14 NASA, necessary data. The data used in this study were ftp://disc2.nascom.nasa.gov/ftp/data/s4pa/TRMM_ acquired as part of the NASA’s Earth – Sun L2/TRMM_2A12/doc/README.TRMM_ System Division and archived and distributed by 2A12.pdf. Last updated in 2008. the Goddard Earth Sciences (GES) Data and 15 NASA, http://mirador.gsfc.nasa.gov/cgi- bin/mirador/presentNavigation.pl?tree=project&pr Information Services centre (DISC). oject= TRMM. Last updated in 2011. 16 Awaka J, Iguchi T & Okamoto K, Rain type References classification algorithm, in: Measuring 1 Seidel, D.J. & Free, M., Comparison of lower- Precipitation from Space Eurainsat and the tropospheric temperature climatologies and trends Future, edited by V. Levizzani, P. Bauer & F. J. at low and high elevation radiosonde sites, Clim. Turf, (Springer, The Netherlands) 2007, pp. 213- Change, 59(2003) 53-74. 224. 2 Houze R A Jr, Convective and stratiform 17 Awaka, J., Iguchi, T. & Okamoto, K., TRMM PR precipitation in the tropics, in: Tropical Rainfall standard algorithm and its performance on bright Measurements, edited by J. S. Theon & N. Fugono, band detection, J. Meteor. Soc. Japan, 87A(2009) (A. Deepak Publishing, Seattle, Washington, USA) 31-52. 1988, pp. 27-35. 18 Singh S, Climatology, (Prayag Pustak Bhawan, 3 Cober, S.G., Strapp, J.W. & Isaac, G.A., An Allahabad, India) 2007, pp. 504. example of super cooled drizzle drops formed 19 Watkins, http://www.applet- through a collision – coalescence process, J. Appl. magic.com/insolation.htm. Meteorol,. 35(1996) 2250-2260. 20 McGregor G R & Nieuwolt S, Tropical 4 Jaiswal R S, Fredrick S R, Neela V S, Rasheed M Climatology (John Wiley and Sons Limited, West & Zaveri L, Study of radar bright band over a Sussex, England) 1998, pp. 339. tropical location, paper presented at the 9th 21 Lydolph P E, Weather and Climate (Rowman and Conference on Coastal Atmospheric and Oceanic Littlefield Publishers, Washington DC, USA) Prediction and Processes, Annapolis, 1985, pp. 216. Maryland, 2010. 5 Harris, G.N.Jr., Bowman, K.P. & Shin, D.B., Comparison of freezing level altitude from the NCEP reanalysis with TRMM precipitation radar bright band data, J. Climate, 13(2000) 4137-4148. 6 Boiser P B & Aceituno P, Changes in surface and upper-air temperature along the arid coast of northern Chile, paper presented at the 8th International Conference on Southern Hemisphere Meteorology and Oceanography, Foz do Iguacu, Brazil, 2006. 7 Bradley, R.S., Keimig, F.T., Diaz, H.F. & Hardy, D.R., Recent changes in freezing level heights in the tropics with implications for the deglacierization of high mountain regions,
SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1085
Appendix
Table 1 — Variation of HFL with LH over Oceanic Sites
Month Model R2 Sig F Std error Equation
Jan 99 None suits Feb 99 None suits Mar 99 None suits Apr 99 None suits May 99 None suits Jun 99 None suits Jul 99 None suits Aug 99 None suits Sep 99 None suits Oct 99 None suits Nov 99 None suits Dec 99 None suits Jan 00 None suits Feb 00 None suits Mar 00 None suits Apr 00 None suits May 00 None suits Jun 00 None suits Jul 00 None suits Aug 00 None suits Sep 00 None suits Oct 00 None suits Nov 00 None suits Dec 00 None suits Jan 01 None suits Feb 01 None suits Mar 01 None suits Apr 01 None suits May 01 None suits Jun 01 None suits Jul 01 None suits Aug 01 None suits Sep 01 None suits Oct 01 None suits Nov 01 None suits Dec 01 None suits Jan 02 None suits Feb 02 None suits Mar 02 None suits Apr 02 Cubic 1.000 0.015 11.547
May 02 None suits Jun 02 None suits Jul 02 None suits Aug 02 None suits Sep 02 None suits Oct 02 None suits Nov 02 None suits Dec 02 None suits Jan 07 None suits Feb 07 None suits Mar 07 None suits Apr 07 None suits May 07 None suits Jun 07 None suits Jul 07 None suits Aug 07 None suits Sep 07 None suits Oct 07 None suits Nov 07 None suits Dec 07 None suits Jan 08 None suits Feb 08 None suits Mar 08 None suits 1086 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015
Apr 08 None suits May 08 Quadratic 0.897 0.011 104.046
Jun 08 None suits Jul 08 None suits Aug 08 None suits Sep 08 None suits Oct 08 Quadratic and 0.831 0.029 40.493 Cubic Nov 08 None suits Dec 08 None suits
Table 2a — Variation of HFL with Temperature over Oceanic Sites
Month Model R2 Sig F Std error Equation
Jan 99 Quadratic and 0.945 0.000 121.491 Cubic Feb 99 Quadratic and 0.951 0.001 132.023 Cubic Mar 99 Quadratic and 0.923 0.000 139.282 Cubic Apr 99 Quadratic and 0.729 0.005 171.386 Cubic May 99 Quadratic and 0.679 0.011 178.418 Cubic Jun 99 None suits Jul 99 Quadratic and 0.811 0.003 103.57 Cubic Aug 99 None suits Sep 99 None suits Oct 99 None suits Nov 99 None suits Dec 99 Quadratic and 0.860 0.007 161.05 Cubic Jan 00 None suits Feb 00 None suits Mar 00 None suits Apr 00 None suits May 00 Quadratic and 0.827 0.002 80.747 Cubic Jun 00 Quadratic and 0.747 0.008 112.249 Cubic Jul 00 Quadratic and 0.824 0.002 100.635 Cubic Aug 00 Quadratic and 0.808 0.003 100.98 Cubic Sep 00 None suits Oct 00 None suits Nov 00 None suits Dec 00 None suits Jan 01 Quadratic 0.729 0.010 86.352
Feb 01 None suits Mar 01 None suits Apr 01 None suits May 01 Quadratic and 0.724 0.040 108.526 Cubic Jun 01 Quadratic and 0.826 0.002 93.918 Cubic Jul 01 Quadratic and 0.774 0.005 114.777 Cubic Aug 01 Linear 0.523 0.043 163.67 Sep 01 None suits Oct 01 None suits Nov 01 None suits Dec 01 None suits Jan 02 Quadratic 0.702 0.027 97.515
Feb 02 None suits SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1087
Mar 02 None suits Apr 02 None suits May 02 None suits Jun 02 Quadratic and 0.850 0.001 87.802 Cubic Jul 02 Quadratic and 0.668 0.037 150.244 Cubic Aug 02 None suits Sep 02 None suits Oct 02 None suits Nov 02 None suits Dec 02 None suits Jan 07 None suits Feb 07 None suits Mar 07 None suits Apr 07 None suits May 07 Cubic 0.642 0.046 118.953
Jun 07 Quadratic and 0.711 0.024 128.493 Cubic Jul 07 Quadratic and 0.653 0.026 62.283 Cubic Aug 07 None suits Sep 07 None suits Oct 07 None suits Nov 07 None suits Dec 07 None suits Jan 08 None suits Feb 08 Quadratic 0.804 0.038 83.101
Mar 08 None suits Apr 08 None suits May 08 Inverse 0.615 0.012 118.93 Jun 08 Quadratic and 0.910 0.002 94.528 Cubic Jul 08 Quadratic and 0.732 0.010 127.03 Cubic Aug 08 None suits Sep 08 None suits Oct 08 None suits Nov 08 None suits Dec 08 None suits
Table 2b — Variation of HFL with Temperature over Continental Locations
Month Model R2 Sig F Std error Equation
Jan 99 Linear 0.751 0.000 306.27 Feb 99 None suits Mar 99 None suits Apr 99 None suits May 99 None suits Jun 99 None suits Jul 99 None suits Aug 99 None suits Sep 99 None suits Oct 99 None suits Nov 99 None suits Dec 99 None suits Jan 00 None suits Feb 00 Linear 0.877 0.019 247.75 Mar 00 None suits Apr 00 None suits May 00 None suits Jun 00 None suits Jul 00 None suits Aug 00 None suits Sep 00 None suits Oct 00 None suits Nov 00 None suits 1088 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015
Dec 00 S 0.847 0.027 0.053
Jan 01 None suits Feb 01 Power 0.900 0.014 0.058 Mar 01 Quadratic 1.000 0.016 16.89 Apr 01 None suits May 01 None suits Jun 01 None suits Jul 01 None suits Aug 01 None suits Sep 01 None suits Oct 01 None suits Nov 01 None suits Dec 01 None suits Jan 02 Quadratic 0.999 0.031 28.701 Feb 02 Linear 0.812 0.037 247.75 Mar 02 None suits Apr 02 None suits May 02 None suits Jun 02 None suits Jul 02 None suits Aug 02 None suits Sep 02 None suits Oct 02 None suits Nov 02 None suits Dec 02 None suits Jan 07 None suits Feb 07 Quadratic 0.983 0.017 114.86 Mar 07 Quadratic 0.999 0.038 59.13 Apr 07 None suits May 07 None suits Jun 07 None suits Jul 07 None suits Aug 07 None suits Sep 07 None suits Oct 07 None suits Nov 07 None suits Dec 07 None suits Jan 08 None suits Feb 08 Cubic 0.974 0.026 140.66 Mar 08 None suits Apr 08 None suits May 08 None suits Jun 08 None suits Jul 08 None suits Aug 08 None suits Sep 08 None suits Oct 08 None suits Nov 08 None suits Dec 08 Quadratic 0.916 0.024 134.69 and Cubic
Table 3 — Variation of HFL with temperature
Location Month R2 Sig F
Trivandrum Jan 98 0.068 0.387 Feb 98 0.086 0.341 Mar 98 0.049 0.509 Apr 98 0.062 0.424 May 98 0.012 0.849 Jun 98 0.047 0.525 Jul 98 0.044 0.529 Aug 98 0.014 0.877 Sep 98 0.101 0.239 Oct 98 0.021 0.745 Nov 98 0.153 0.099 Dec 98 0.067 0.379 Bangalore Jan 98 0.131 0.140 Feb 98 0.094 0.338 SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1089
Mar 98 0.063 0.446 Apr 98 0.041 0.777 May 98 0.045 0.537 Jun 98 0.056 0.500 Jul 98 0.077 0.432 Aug 98 0.058 0.491 Sep 98 0.135 0.338 Oct 98 0.070 0.541 Nov 98 0.053 0.493 Dec 98 0.221 0.034
Table 4 — Variation of HFL with Elevation over Continental Locations
S.No Month Model 1 Jan 99 None suits 2 Feb 99 None suits 3 Mar 99 None suits 4 Apr 99 None suits 5 May 99 None suits 6 Jun 99 None suits 7 Jul 99 None suits 8 Aug 99 None suits 9 Sep 99 None suits 10 Oct 99 None suits 11 Nov 99 None suits 12 Dec 99 None suits 13 Jan 00 None suits 14 Feb 00 None suits 15 Mar 00 None suits 16 Apr 00 None suits 17 May 00 None suits 18 Jun 00 None suits 19 Jul 00 None suits 20 Aug 00 None suits 21 Sep 00 None suits 22 Oct 00 None suits 23 Nov 00 None suits 24 Dec 00 None suits 25 Jan 01 None suits 26 Feb 01 None suits 27 Mar 01 None suits 28 Apr 01 None suits 29 May 01 None suits 30 Jun 01 None suits 31 Jul 01 None suits 32 Aug 01 None suits 33 Sep 01 None suits 34 Oct 01 None suits 35 Nov 01 None suits 36 Dec 01 None suits 37 Jan 02 None suits 38 Feb 02 None suits 39 Mar 02 None suits 40 Apr 02 None suits 41 May 02 None suits 42 Jun 02 None suits 43 Jul 02 None suits 44 Aug 02 None suits 45 Sep 02 None suits 46 Oct 02 None suits 47 Nov 02 None suits 48 Dec 02 None suits 49 Jan 07 None suits 50 Feb 07 None suits 51 Mar 07 None suits 52 Apr 07 None suits 53 May 07 None suits 54 Jun 07 None suits 55 Jul 07 None suits 56 Aug 07 None suits 1090 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015
57 Sep 07 None suits 58 Oct 07 None suits 59 Nov 07 None suits 60 Dec 07 None suits 61 Jan 08 None suits 62 Feb 08 None suits 63 Mar 08 None suits 64 Apr 08 None suits 65 May 08 None suits 66 Jun 08 None suits 67 Jul 08 None suits 68 Aug 08 None suits 69 Sep 08 None suits 70 Oct 08 None suits 71 Nov 08 None suits 72 Dec 08 None suits
Table 5 — Comparison of Model derived HFL with ITU-R model and NOAA
Location Month HFL value from HFL value from ITU- Average HFL value from Model (m) R (m) NOAA (m)
Mozambique January 4361.28474 5000
(-17.8, 38.18) February 4420.29118 5000
March 4398.16519 5000
April 4228.83082 5000
May 4062.02466 5000
June 4013.84643 5000
July 3925.32086 5000
August 3875.03143 5000
September 3835.03059 5000
October 3896.71601 5000
November 4002.15506 5000
December 4281.67314 5000
Pacific Ocean January 4397.409 5000
(-17, -164) February 4458.626 5000
March 4439.041 5000
April 4273.929 5000
May 4109.282 5000
June 4054.688 5000
July 3961.523 5000
August 3919.497 5000
September 3881.784 5000
October 3941.941 5000
November 4044.386 5000
December 4319.466 5000
Papua New Guinea January 4753.648 5000
(-4, 147) February 4825.85 5000
March 4868.223 5000
April 4823.894 5000
May 4715.108 5000
June 4603.691 5000
July 4470.226 5000
August 4478.055 5000
September 4467.473 5000
SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1091
October 4507.35 5000
November 4564.048 5000
December 4694.711 5000
Indian Ocean January 4767.312 5000
(0, 90) February 4833.442 5000
March 4897.871 5000
April 4903.218 5000
May 4824.336 5000
June 4715.835 5000
July 4586.902 5000
August 4586.543 5000
September 4580.501 5000
October 4612.234 5000
November 4651.116 5000
December 4715.991 5000
Trivandrum January 4633.83563 5000 4949.82765 (8.29, 76.59) February 4673.21952 5000 4907.04762 March 4777.53589 5000 4846.24405
April 4889.12318 5000 4818.37619
May 4901.57614 5000 4850.32759
June 4834.83708 5000 4845.58871
July 4748.39574 5000 4725.03333
August 4713.85175 5000 4759.2149
September 4711.51339 5000 4793.14593
October 4715.78525 5000 4789.01724
November 4701.83337 5000 4789.22857
December 4611.25437 5000 4764.77778
Panama January 4598.24 5000
(8.5, -79.5) February 4634.4446 5000
March 4742.22 5000
April 4871.77563 5000
May 4913.60625 5000
June 4793.45024 5000
July 4671.61231 5000
August 4649.83575 5000
September 4690.20775 5000
October 4697.17488 5000
November 4688.15163 5000
December 4596.81888 5000
Costa Rica January 4605.77754 5000
(9.18, -85.43) February 4641.15435 5000
March 4748.85521 5000
April 4871.46575 5000
May 4896.50933 5000
June 4837.33343 5000
July 4758.43123 5000
August 4719.59046 5000
September 4717.19219 5000
October 4717.34418 5000
1092 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015
November 4696.01355 5000
December 4587.75138 5000
Salem January 4597.16506 5000
(11.39, 78.12) February 4507.10805 5000
March 4664.26142 5000
April 4774.09805 5000
May 4883.24003 5000
June 4738.19901 5000
July 4115.35639 5000
August 4568.54889 5000
September 4603.84393 5000
October 4700.18449 5000
November 4761.69172 5000
December 4650.8197 5000
Bangalore January 4596.89059 5000 5061.77778 (12.58, 72.38) February 4471.09063 5000 4988.58333 March 4641.522 5000 4855.89516
April 4739.9622 5000 4762.68333
May 4908.52613 5000 4723.04209
June 4772.38851 5000 5013.27198
July 3976.47421 5000 4944.95
August 4576.99862 5000 4962.21736
September 4612.13652 5000 4980.92667
October 4703.13968 5000 4897.99761
November 4779.48714 5000 5073.73333
December 4653.54336 5000 5006.48
Chennai January 4475.14738 5000 5143.83058 (13.04, 80.17) February 4502.62428 5000 5083.49754 March 4636.2497 5000 4959.95161
April 4815.84612 5000 4901.47857
May 4956.63845 5000 4759.05269
June 4844.26984 5000 4885.26786
July 4734.03803 5000 4906.03226
August 4719.97818 5000 4914.30645
September 4787.47722 5000 4913.86667
October 4750.31485 5000 4935.91268
November 4678.42195 5000 5068.27586
December 4520.161 5000 4970.55333
Panjim January 4380.54208 5000 4724.4 (15.3, 73.55) February 4402.90596 5000 4622.5 March 4548.34398 5000 4472.78879
April 4750.9265 5000 4538.34439
May 4950.61498 5000 4702.89034
June 4841.99093 5000 4654.883
July 4740.97486 5000 4796.35989
August 4732.5738 5000 4843.82143
September 4817.84892 5000 4791.30423
October 4751.73393 5000 4600.8141
November 4645.30204 5000 4744.15667
SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1093
December 4454.09878 5000 4878.23913
Machilipatnam January 4341.8806 5000 5060.35789 (16.09, 81.12) February 4362.33671 5000 4926.235 March 4511.70185 5000 4976.50575
April 4721.92623 5000 4710.06211
May 4943.73096 5000 4674.62993
June 4836.47379 5000 5096.22365
July 4739.29504 5000 5217.6366
August 4733.18443 5000 5161.68875
September 4825.34315 5000 5164.89245
October 4747.95752 5000 5117.45037
November 4628.94386 5000 5140.20051
December 4426.35722 5000 5007.45334
Visakhapatnam January 4270.00213 5000 4916.80208 (17.42, 83.5) February 4287.10544 5000 4704.35714 March 4442.82091 5000 4615.24048
April 4665.38892 5000 4569.59762
May 4926.22853 5000 4528.18519
June 4821.39349 5000 4972.17888
July 4731.44761 5000 5042.25753
August 4729.57714 5000 5019.12843
September 4834.10856 5000 5020.55513
October 4736.36551 5000 4905.56483
November 4595.57494 5000 4967.5375
December 4374.02755 5000 4888.34615
Mumbai January 4202.05744 5000 4616.625 (18.55, 72.54) February 4216.17315 5000 4269.9 March 4377.00848 5000 4298.08333
April 4609.50437 5000 4079.5
May 4905.29374 5000 4627.44444
June 4802.67466 5000 5010.125
July 4719.67361 5000 4974.11905
August 4721.79787 5000 4961.46429
September 4837.61422 5000 5096.41667
October 4721.1846 5000 4494.22222
November 4561.30532 5000 4718
December 4323.88537 5000 4508
South China Sea January 4107.56791 5000
(18.97, 113.4) February 4084.33346 5000
March 4209.84124 5000
April 4436.9114 5000
May 4644.02707 5000
June 4711.42467 5000
July 4759.50496 5000
August 4679.26322 5000
September 4670.35068 5000
October 4604.23921 5000
November 4471.96868 5000
December 4166.38945 5000
1094 INDIAN J. MAR. SCI., VOL. 44, NO. 7 JULY 2015
Calcutta January 3919.69149 5000 4327.85714 (22.34, 88.29) February 2739.86367 5000 3989.08333 March 3302.02211 5000 3942.72222
April 3336.67227 5000 4474.70833
May 4560.01969 5000 4479.58289
June 4769.50666 5000 5427.26136
July 982.690836 5000 5298.425
August 4731.09498 5000 5460
September 4757.32546 5000 5064.0625
October 4727.06601 5000 4887.07143
November 4754.97579 5000 4781.5
December 4102.07258 5000 4543.83333
Gulf of Mexico January 3804.2967 5000
(22.9, -94.2) February 3750.03911 5000
March 3869.52795 5000
April 4125.1431 5000
May 4430.97056 5000
June 4573.12426 5000
July 4697.25985 5000
August 4608.93668 5000
September 4594.38962 5000
October 4488.51354 5000
November 4293.2032 5000
December 3910.90875 5000
Taiwan January 3684.602 4850
(25, 121) February 3679.22 4850
March 3863.562 4850
April 4140.706 4850
May 4667.814 4850
June 4582.4965 4850
July 4555.0295 4850
August 4587.507 4850
September 4781.329 4850
October 4532.476 4850
November 4253.323 4850
December 3931.213 4850
Guwahati January 3554.32029 4766 3045.5 (26.12, 91.42) February 1558.91569 4766
March 2318.39396 4766
April 2278.21953 4766 4181.42308
May 4192.29883 4766 5011.53571
June 4573.28848 4766 5074.12
July -1434.8012 4766 5063.14286
August 4541.38408 4766 4883.1
September 4584.1384 4766 4774
October 4528.71049 4766 4761
November 4709.58383 4766 4608.73333
December 3790.10469 4766 4101.3
New Delhi January 3371.35206 4596.5 3213.91667 SEN JAISWAL et al.: STUDY OF TRMM ESTIMATED FREEZING LEVEL HEIGHT IN THE 30N-30S REGION 1095
(28.38, 77.12) February 767.388852 4596.5 2849.625 March 1631.3034 4596.5 3694.25
April 1514.62109 4596.5 3132
May 3919.94552 4596.5 4359.375
June 4386.93522 4596.5 5329.63333
July -3321.4909 4596.5 5340.75
August 4278.48098 4596.5 5348.08929
September 4343.1996 4596.5 5139.75
October 4295.38854 4596.5 3963.6
November 4685.95461 4596.5
December 3627.81221 4596.5 3603
East China Sea January 3094.992 4475
(30, 123) February 2973.802 4475
March 3056.771 4475
April 3334.728 4475
May 3862.626 4475
June 4177.875 4475
July 4480.732 4475
August 4401.623 4475
September 4372.511 4475
October 4170.784 4475
November 3828.186 4475
December 3319.071 4475
Mediterranean Sea January 2370.84794 4049.8
(35.67, 12.12) February 2186.14186 4049.8
March 2211.39822 4049.8
April 2472.91884 4049.8
May 3223.43445 4049.8
June 3714.50469 4049.8
July 4201.91672 4049.8
August 4160.64823 4049.8
September 4115.85006 4049.8
October 3811.74942 4049.8
November 3315.81236 4049.8
December 2722.35166 4049.8