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S. Bony, Y. Sud, K. M. Lau, J. Susskind, and S. Saha


This study compares the atmospheric reanalyses that have been produced independently at the Data Assimilation Office (DAO) of Goddard Laboratory for Atmospheres and at the National Centers for Environmental Prediction (NCEP). These reanalyses were produced by using a frozen state-of-the-art version of the global data assimilation system developed at these two centers. For the period 1987–88 and for the tropical oceanic regions of 30°S–30°N, surface and atmospheric fields related to atmospheric hydrology and radiation are compared and assessed, wherever possible, with satellite data. Some common biases as well as discrepancies between the two independent reassimilation products are highlighted.

Considering both annual averages and interannual variability (1987–88), discrepancies between DAO and NCEP reanalysis in water vapor, precipitation, and clear-sky longwave radiation at the top of the atmosphere are generally smaller than discrepancies that exist between corresponding satellite estimates. Among common biases identified in the reanalyses, the authors note an underestimation of the total precipitable water and an overestimation of the shortwave cloud radiative forcing in warm convective regions. Both lead to an underestimation of the surface radiation budget. The authors also note an overestimaton of the clear-sky outgoing longwave radiation in most tropical ocean regions, as well as an overestimation of the longwave radiative cooling at the ocean surface.

Surface latent and sensible heat fluxes differ by about 20 and 3 W m−2, respectively, in the two reanalyses. Differences in the surface radiation budget are larger than the uncertainties of satellite-based estimates. Biases in the surface radiation fluxes derived from the reanalyses are primarily due to incorrect shortwave cloud radiative forcing and, to a lesser degree, due to a deficit in the total precipitable water and a cold bias at lower-tropospheric temperatures.

This study suggests that individual features and biases of each set of reanalyses should be carefully studied, especially when using analyzed surface fluxes to force other physical or geophysical models such as ocean circulation models. Over large regions of the tropical oceans, DAO and NCEP reanalyses produce surface net heat fluxes that can differ by up to 50 W m−2 in the average and by a factor of 2 when considering interannual anomalies. This may lead to vastly different thermal forcings for driving ocean circulations.

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P. Ernest Raj, P. C. S. Devara, R. S. Maheskumar, G. Pandithurai, K. K. Dani, S. K. Saha, S. M. Sonbawne, and Y. K. Tiwari


A compact, hand-held multiband sun photometer (ozone monitor) has been used to measure total precipitable water content (PWC) at the low-latitude tropical station in Pune, India (18°32′N, 73°51′E). Data collected in the daytime (0730–1800 LT) during the period from May 1998 to September 2001 have been used here. The daytime average PWC value at this station is 1.13 cm, and the average for only the clear-sky days is 0.75 cm. PWC values between 0.75 and 1.0 cm have the maximum frequency of occurrence. There is a large day-to-day variability due to varied sky and meteorological conditions. Mainly two types of diurnal variations in PWC are observed. The one occurs in the premonsoon summer months of April and May and shows that forenoon values are smaller than afternoon values. The other type occurs in November and December and shows a minimum around noontime. There is a diurnal asymmetry in PWC in which, on the majority of the days, the mean afternoon value is greater than the forenoon value. This asymmetry is more pronounced in the summer and southwest monsoon months (i.e., March–June). Monthly mean PWC is highest in September and lowest in December. The increase in PWC from the winter (December–February) to summer (March–May) seasons is about 50% and from the summer to southwest monsoon seasons (June–September) is almost 98%. Sun photometer–derived PWC shows a fairly good relationship with surface relative humidity and radiosonde-derived PWC, with a correlation coefficient as high as 0.80.

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K. Sujith, Subodh Kumar Saha, Samir Pokhrel, Anupam Hazra, and Hemantkumar S. Chaudhari


This study estimates the seasonal mean (June–September) recycled rainfall and investigates its dominant modes of variability over the continental regions of the Indian summer monsoon. A diagnostic method based on the basic atmospheric water vapor budget equation is employed in order to partition the observed rainfall into recycled and advected components. The global teleconnections with the recycled (advected) rainfall are found to be weak (strong), which is consistent with the basic assumptions of the sources of atmospheric water vapor. It is shown that the mean recycled rainfall over the Indo-Gangetic Plain, central India, and western Himalayas ranges between 10% and 40% of the total rainfall. While EOF1 (38.5%) of the recycled rainfall reveals covariability between the regional and external influences, EOF2 (14%) shows a mode independent to the external influences (i.e., advected rainfall), prevailing over the Indo-Gangetic Plain. Furthermore, a strong decreasing trend in PC2 over the last 36 years suggests a change in the local feedback (land, atmosphere), which in turn may have also contributed to the decreasing trend in the observed monsoon rainfall over central and northern India.

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P. C. S. Devara, P. E. Raj, K. K. Dani, G. Pandithurai, M. C. R. Kalapureddy, S. M. Sonbawne, Y. J. Rao, and S. K. Saha


Lidar profiling of atmospheric aerosols and clouds in the lower atmosphere has been in progress at the Indian Institute of Tropical Meteorology (IITM), Pune (18°32′N, 73°52′E, 559 m MSL), India, for more than two decades. To enlarge the scope of these studies, an eye-safe new portable dual polarization micropulse lidar (DPMPL) has been developed and installed at this station. The system utilizes a diode-pumped solid-state (DPSS) neodymium–yttrium–aluminum–garnet (Nd:YAG) laser second harmonic, with either parallel polarization or alternate parallel and perpendicular polarization, as a transmitter and a Schmidt–Cassegrain telescope, with a high-speed detection and data acquisition and processing system, as a receiver. This online system in real-time mode provides backscatter intensity profiles up to about 75 km at every minute in both parallel and perpendicular polarization channels, corresponding to each state of polarization of the transmitted laser radiation. Thus, this versatile lidar system is expected to play a vital role not only in atmospheric aerosol and cloud physics research and environmental monitoring but also in weather and climate modeling studies of the impact of radiative forcing on the earth–atmosphere radiation balance and hydrological cycle. This paper provides a detailed description of Asia’s only lidar facility and presents initial observations of space–time variations of boundary layer structure from experiments carried out during winter 2005/06.

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Gayatri Vani K., Greeshma M. Mohan, Anupam Hazra, S. D. Pawar, Samir Pokhrel, Hemantkumar S. Chaudhari, Mahen Konwar, Subodh K. Saha, Chandrima Mallick, Subrata K. Das, Sachin Deshpande, Sachin D. Ghude, Manoj Domkawale, Suryachandra A. Rao, Ravi. S. Nanjundiah, and M. Rajeevan


The evaluation and usefulness of lightning prediction for the Indian subcontinent are demonstrated. Implementation of the lightning parameterizations based on storm parameters, in the Weather Research and Forecasting (WRF) Model, with different microphysics schemes are carried out. With the availability of observed lightning measurements over Maharashtra from the lightning detection network (LDN), lightning cases have been identified during the pre-monsoon season of 2016–18. Lightning parameterization based on cloud top height defined by a reflectivity threshold factor of 20 dBZ is chosen. Initial analysis is carried out for 16 lightning events with four microphysical schemes for the usefulness in lightning prediction. Objective analysis is carried out and quantitative model performance (skill scores) is assessed based on observed data. The skills are evaluated for 10- and 50-km2 boxes from the 1-km domain. There is good POD of 0.86, 0.82, 0.85, and 0.84, and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from WSM6, Thompson, Morrison, and WDM6, respectively. There is an overestimation in lightning flash with a spatial and temporal shift. The fractional skill score is evaluated as a function of spatial scale with neighborhoods from 25 to 250 km. These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast over India. The skill for 2019 and 2020 pre-monsoon are calculated to address the predictability of operational lightning prediction over India.

Significance Statement

A high-resolution model, namely, the Weather Research and Forecasting (WRF) Model, with multiple microphysics parameterization schemes and lightning parameterization is used here. The objective analysis is carried out for the lightning cases over India and the quantitative performance is assessed. The results highlight that there is fairly good probability of detection (POD) of 0.86, 0.82, 0.85, and 0.84 and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from four different microphysical schemes (WSM6, Thompson, Morrison, and WDM6, respectively). These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast. The validation of lightning forecast system deployed over India for five pre-monsoon months in real time is carried out, which gives POD of 0.90, FAR of 0.64, hit rate of 0.57, and POFD of 0.50 for the whole Indian region.

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Suryachandra A. Rao, B. N. Goswami, A. K. Sahai, E. N. Rajagopal, P. Mukhopadhyay, M. Rajeevan, S. Nayak, L. S. Rathore, S. S. C. Shenoi, K. J. Ramesh, R. S. Nanjundiah, M. Ravichandran, A. K. Mitra, D. S. Pai, S. K. R. Bhowmik, A. Hazra, S. Mahapatra, S. K. Saha, H. S. Chaudhari, S. Joseph, P. Sreenivas, S. Pokhrel, P. A. Pillai, R. Chattopadhyay, M. Deshpande, R. P. M. Krishna, Renu S. Das, V. S. Prasad, S. Abhilash, S. Panickal, R. Krishnan, S. Kumar, D. A. Ramu, S. S. Reddy, A. Arora, T. Goswami, A. Rai, A. Srivastava, M. Pradhan, S. Tirkey, M. Ganai, R. Mandal, A. Dey, S. Sarkar, S. Malviya, A. Dhakate, K. Salunke, and Parvinder Maini


In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

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E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, Roy Jenne, and Dennis Joseph

The NCEP and NCAR are cooperating in a project (denoted “reanalysis”) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957–96. This eliminates perceived climate jumps associated with changes in the data assimilation system.

The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided by different countries and organizations. The system has been designed with advanced quality control and monitoring components, and can produce 1 mon of reanalysis per day on a Cray YMP/8 supercomputer. Different types of output archives are being created to satisfy different user needs, including a “quick look” CD-ROM (one per year) with six tropospheric and stratospheric fields available twice daily, as well as surface, top-of-the-atmosphere, and isentropic fields. Reanalysis information and selected output is also available on-line via the Internet (http// A special CDROM, containing 13 years of selected observed, daily, monthly, and climatological data from the NCEP/NCAR Reanalysis, is included with this issue. Output variables are classified into four classes, depending on the degree to which they are influenced by the observations and/or the model. For example, “C” variables (such as precipitation and surface fluxes) are completely determined by the model during the data assimilation and should be used with caution. Nevertheless, a comparison of these variables with observations and with several climatologies shows that they generally contain considerable useful information. Eight-day forecasts, produced every 5 days, should be useful for predictability studies and for monitoring the quality of the observing systems.

The 40 years of reanalysis (1957–96) should be completed in early 1997. A continuation into the future through an identical Climate Data Assimilation System will allow researchers to reliably compare recent anomalies with those in earlier decades. Since changes in the observing systems will inevitably produce perceived changes in the climate, parallel reanalyses (at least 1 year long) will be generated for the periods immediately after the introduction of new observing systems, such as new types of satellite data.

NCEP plans currently call for an updated reanalysis using a state-of-the-art system every five years or so. The successive reanalyses will be greatly facilitated by the generation of the comprehensive database in the present reanalysis.

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