This study contributes to the estimation of the global mean and zonal distribution of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference to assess current precipitation products. Precipitation estimates from the
Precipitation is essential for life and plays an impor- tant role in the energy balance of the planet (Kiehl and Trenberth 1997; Trenberth et al. 2009; Stephens et al. 2012; Wong et al. 2014). Quantifying the amount and distribution of precipitation is critical for understanding the current state of Earth's climate and future changes (Stephens et al. 2012; Trenberth et al. 2007). Latent heat flux is commonly inferred from precipitation measure- ment; hence any long-term change in precipitation amount implies a change in evaporation to sustain their balance (Stephens et al. 2012). Current climate models contain serious biases in the modeling and prediction of precipitation (Stephens et al. 2010), but it is also im- portant to realize that our current precipitation obser- vations are not perfect (Behrangi et al. 2012). Recognizing the importance of accurate estimation of precipitation climatology, the
A major goal of these products is to provide a consis- tent long time series of monthly and finer time resolu- tion precipitation analyses on a global scale. GPCP products are available from the primary monthly prod- uct to pentad (Xie et al. 2003) and daily (Huffman et al. 2001) time scales. The global long-term data are ob- tained by merging rain data from gauges (restricted to over land) and spaceborne sensors, including Special Sensor Microwave Imager and Special Sensor Micro- wave Imager/Sounder, and geostationary and polar- orbiting infrared imagers and sounders. The GPCP merger procedure uses more accurate estimates of pre- cipitation (e.g., gauges over land and passive micro- wave) to adjust the bias in other estimates (e.g., from infrared imagers and sounders) and then combines the estimates with an inverse error weighting technique. The combination of data from these multiple sensors/ sources remains a challenging task as there are time and space discontinuities in the datasets due to instrumen- tation and algorithm changes. Another challenge that GPCP faces is high-latitude precipitation estimation, as the current retrieval algorithms that are based on infrared and microwave sensors are not robust enough to retrieve accurate rain and snow rates (Liu 2008; Behrangi et al. 2012). GPCP infers the high-latitude precipitation (including snow) rate using a regression relationship between collocated rain gauge measurements (adjusted for wind loss, including relation to snow) and a few cloud-related parameters (e.g., cloud-top pressure, frac- tional cloud cover, and cloud-layer relative humidity) obtained from Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) and Atmospheric Infrared Sounder (AIRS) data (Susskind et al. 1997; Adler et al. 2003; Huffman et al. 2009). A recent comparison of high-latitude GPCP precipitation estimates with gauge observations over
CMAP provides gridded global monthly estimate of precipitation using many of the same datasets as GPCP, plus Microwave Sounding Unit (MSU) data. However, the merging of the individual data sources is different from GPCP. Using gauge-based analysis and atoll gauge data, the individual random error is defined for each grid and for each month and the satellite estimates are com- bined linearly through the maximum likelihood estima- tion method to reduce the random error. Postprocessing is then carried out to reduce biases by comparing the estimates to the atoll rain gauge data over the tropics and by subjective assumption regarding the bias structure over the extratropics (Xie and Arkin 1997). CMAP im- plicitly accounts for snowfall using microwave sensors with significant shortcomings for precipitation retrievals at high latitudes.
Arguably, the recent higher-quality precipitation es- timates from spaceborne sensors should be used to provide an independent reference or guideline to assess or improve precipitation records such as GPCP and CMAP. The CloudSat Cloud Profiling Radar (CPR) and TRMM Precipitation Radar (PR) are among the recent sensors that can provide unprecedented accuracies and sensitivities to estimate precipitation rates from snow and drizzle all the way to intense rainfall. Berg et al. (2010) showed that the precipitation estimates from the two radars are complementary to construct a merged distribution of rain volume spanning a broad range of rain intensities. Behrangi et al. (2012) constructed a merged distribution of rain volume over ocean between 608S and 608N using TRMM and CloudSat and used the merged distribution as a reference to evaluate the per- formance of precipitation retrievals from several space- borne instruments.
The present paper extends the previous work by Behrangi et al. (2012) by 1) extending the study area to almost the entire CloudSat sampled region (808S-808N), 2) including global snow rate from recently developed and modified snow retrieval algorithms, and 3) com- paring the amount and distribution of the merged pre- cipitation product with the most recent version of GPCP (version 2.2) and CMAP after accounting for the diurnal cycle of precipitation. Several other improvements are also included in the present work, which will be dis- cussed in section 3. The outcome of this study provides a new estimate of near-global ocean precipitation rate and its zonal distribution using advanced precipitation measuring sensors and serves as an independent refer- ence to assess precipitation products over the ocean, where due to lack of accurate ground observations evaluation of the climatological absolute magnitude has been problematic (Adler et al. 2012; Tian and Peters- Lidard 2010).
2. Data resources
The following primary datasets from three years (2007-09) of the most recently updated versions are used in this study: (a) CloudSat rain estimate from Release-04 2C-RAIN-PROFILE developed by Mitrescu et al. (2010) and modified by Lebsock and L'Ecuyer (2011),(b)CloudSat snow rate from 2C-SNOW- PROFILE (described in http://www.cloudsat.cira.colostate. edu) as well as another product by Liu (2008) that in- cludes the latest modifications based on some recent field comparisons, (c) PR rain rate from the official TRMM PR 2A25 version 7 products based on the original algorithm developed by Iguchi et al. (2000) together with series of revisions and modifications (Iguchi 2011), (d) the latest version of monthly GPCP precipitation rate (ver- sion 2.2; Huffman and Bolvin 2012), and (e) the CMAP product.
The present work also made use of four additional data sources: (a) the TRMM Combined Instrument (TCI) estimate (version 7), which employs data from both TMI and TRMM PR (TRMM product 2B31; Haddad et al. 1997); (b) the gridded high resolution (0.25830.258,3h) precipitation product from the CPC morphing algo- rithm (CMORPH; Joyce et al. 2004); (c) precipitation frequency from CloudSat release-04 2C-PRECIP- COLUMN product, developed by Haynes et al. (2009); and (d) Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) rain rate (Wilheit et al. 2003) collocated to CloudSat footprints. The col- located dataset was obtained from the AMSR-E auxil- iary product (Release-04) through the data processing center (http://www.cloudsat.cira.colostate.edu).
3. Methodology and results
A thorough estimation of the amount and distribution of global precipitation requires the inclusion of both liquid and solid phases of precipitation. Figure 1 shows 2D frequency maps of total oceanic precipitation (Fig. 1a) and rain (Fig. 1b), snow (Fig. 1c), and mixed phase (Fig. 1d) between 808Sand808N obtained from the CloudSat 2C-PRECIP-COLUMN product (Haynes et al. 2009). Mean zonal distribution of precipitation frequencies is also shown in Fig. 2. The 94-GHz (W band) Cloud Profiling Radar (CPR) aboard CloudSat (Stephens et al. 2008), with a minimum detectable signal of ;228 dBZ, has provided an unprecedented opportunity to measure snow, drizzle, and light rainfall that goes undetected by other sensors (Behrangi et al. 2012). The zonal distri- bution of precipitation is not symmetric, and in both hemispheres, poleward of ;558 latitude, the solid phase of precipitation gradually becomes dominant (Fig. 2). At ;608S, where the precipitation frequency is the largest (more than 23%), only about 11% of all precipitation events occur in the form of rain. This implies that for the estimation of global precipitation, it is important to be able to distinguish between pre- cipitation phases. This is a challenging task given that currently the orbital precipitation products used in GPCP and CMAP do not directly capture solid-phase precipitation intensities. In GPCP snow rate is esti- mated indirectly using regression relationship between a few collocated rain gauge measurements and cloud- related parameters from infrared sounders, and CMAP accounts for snowfall only implicitly. In this paper global precipitation rate is calculated through the steps de- scribed below.
a. Rain rate estimation
Among the satellite sensors the 94-GHz (W band) CPR offers the highest sensitivity to capture the occur- rence and intensity of drizzle, light rain, and snowfall. On the other hand, the 13.8-GHz PR captures moderate and intense rainfall over tropics, but due to its minimum detectable signal of about 17 dBZ it has a limited sensitivity to detect and estimate light rainfall. The latest version of the CloudSat rainfall product, 2C-RAIN- PROFILE, uses the path-integrated attenuation in addi- tion to the observed reflectivity profile while implementing relatively more realistic assumptions regarding the vertical distribution of rainwater and the rainfall drop size distribution compared the previous algorithm. This makes the 2C-RAIN-PROFILE product more appro- priate for the retrieval of warm rainfall (Lebsock and L'Ecuyer 2011).
By recognizing the complementary information of the two sensors, a merged distribution of rain volume was constructed from the rain volume distribution of CloudSat and PR. The rain volume distribution is a plot of rain intensity versus normalized rain volume within each intensity bin, so that the area below the distribution is unity [see Fig. 9 of Behrangi et al. (2012) for a detailed description of the merging process]. In brief, the merg- ing process comprises three steps. 1) The rain volume distribution is created from CloudSat and PR estimates. 2) The CloudSat rain intensities less than 1 mm h21 and PR rain intensities greater than 1 mm h21 are trusted and used to merge the two rain volume distributions. 3) A constraint is set, so the frequency of rain incidences from the merged PR and CloudSat distribution does not exceed the total count of CloudSat rain incidences. Prior to the count of CloudSat rain incidences, rain rates from five neighboring CPR footprints were averaged to ac- count for the differences in footprint size of PR and CPR as discussed in Behrangi et al. (2012). That study also shows that the effect of the CPR averaging scale is al- most negligible when matching to the PR footprint size. In the extratropics, where TRMM PR is not available, precipitation retrievals from Aqua's AMSR-E can sig- nificantly underestimate light rainfall and snow com- pared to that estimated from CloudSat (Behrangi et al. 2012). Conversely, in this region up to about 5% of rain events captured by CloudSat face a signal saturation problem under the heaviest rainfall (Stephens et al. 2008). Although it is possible to make adjustments to account for these heavy rainfall cases (Tanelli et al. 2008), we chose to use collocated AMSR-E rain rates that include extratropical intense rainfall in the calcu- lations. CloudSat CPR and AMSR-E fly in formation as part of the A-Train, which makes it relatively simple to match up the observations.
b. Snow rate estimation
The emergence of high-frequency radar on CloudSat with a minimum detectable signal of ;228 dBZ created an excellent opportunity to advance high-latitude pre- cipitation studies. Liu (2008) developed an algorithm (the algorithm is hereafter referred to as Liu08) to estimate snowfall rate from CloudSat measurements. In brief, Liu08 implements a two-step process to re- trieve snow rate from CloudSat: 1) distinguishing be- tween liquid and solid phase of precipitation based on ground measurements and weather reports and by identifying a temperature threshold based on condi- tional probability of solid precipitation as a function of surface air temperature, and 2) converting radar re- flectivity to snowfall rate based on backscatter com- putations of nonspherical ice particles and in situ measured particle distributions. The original rain-snow separation temperature was modified later (18Cinstead of 28C) as a result of some recent field measurements [G. Liu (the algorithm developer) 2013, personal com- munication]. Note that while many factors contribute to the uncertainties in snowfall retrieval rates (Hiley et al. 2011), the uncertainty in knowing the snow particle shape and size distribution is a major source of random error for the reflectivity-snowfall rate relation, which could be about 50% (Liu 2008). A more recent snowfall rate product, 2C-SNOW-PROFILE, is also considered in our calculations. 2C-SNOW-PROFILE retrieves profile of snowfall rate through multiple steps. It first uses the 2C- PRECIP-COLUMN product to identify snowfall and then uses reflectivity and cloud mask and temperature profiles to locate snow layers. An optimal estimation al- gorithm is used to retrieve profiles of size distribution parameters using a priori information about snow mi- crophysical properties, radar scattering properties, and size distribution parameters. Profiles of snowfall rates and snow water contents are then computed using the re- trieved size distribution parameter profiles and the a pri- ori information. The surface snowfall rate is obtained from estimated snow properties in the bottommost por- tion of the retrieved snow rate profile (a full description of the retrieval algorithm can be obtained from http:// www.cloudsat.cira.colostate.edu). The total precipitation was calculated by adding CloudSat snow rate (form the latest update of the algorithm) to the merged CloudSat- TRMM rainfall rate.
c. Accounting for diurnal cycle of precipitation
The merged CloudSat-TRMM precipitation product provides an estimate of mean precipitation rate at CloudSat equator crossing times (;01:30 and 13:30 LT). While it is known that the diurnal cycle of mean pre- cipitation rate is not strong over ocean, for a thorough daily estimate one should consider the effect of diurnal cycle of precipitation. Therefore, the diurnal cycle of precipitation was calculated for each 108 latitudinal band from 558Sto558N and the ratios of daily mean precipitation to mean precipitation at CloudSat equator- crossing times were calculated. The ratios were then used as adjustment factors to convert mean precipitation rate at the CloudSat equator-crossing times to a daily mean rate. Poleward of 558 latitude in both hemispheres, no diurnal cycle adjustment was computed because of the lack of reliable subdaily precipitation retrievals. The adjustment factors were calculated using two products: 1) TRMM 2B31 (Haddad et al. 1997) precipitation rate between 388S and 388N because TRMM flies in a sun nonsynchronous orbit and 2B31 has been recognized as a high-quality product (Huffman et al. 2007), and 2) CMORPH for the latitude band 408-558 in both hemispheres, mainly because CMORPH exclusively uses microwave-based precipitation estimates, and precipitation estimates from microwave sensors are considered more skillful than IR-based products (Adler et al. 2001; Ebert et al. 2007; Behrangi et al. 2009, 2010), especially at high latitudes where convective clouds are less frequent. Fig- ure 3 shows that the adjustment factors are fairly close to unity across all latitudes. Therefore, even without any adjustment, the precipitation rate at CloudSat equator- crossing times provides a good approximation for daily mean precipitation over ocean. Poleward of 608S/N, where CMORPH does not provide precip- itation estimates, no adjustment is considered.
d. Zonal distribution of precipitation and comparison with GPCP
Zonal distributions of 3-yr (2007-09) mean pre- cipitation rates from MCTA (hereafter MCTA refers to the merged precipitation estimate from the CloudSat, TRMM, and Aqua platforms) with snow rate from 2C- SNOW-PROFILE, and the latest versions of GPCP (V2.2) and CMAP are shown in Fig. 4a. Rain-only rates are also shown to distinguish the contribution of snow- fall at high latitudes. In addition, by subtracting MCTA precipitation rates from those of GPCP and CMAP, zonal differences (ZD) between GPCP and MCTA and CMAP and MCTA are displayed in Fig. 4b. Relative differences (RD) between the two products and MCTA were calculated by dividing the ZD of each pair by their mean in each 2.58 zonal bin (Fig. 4c). Compared to MCTA, GPCP and CMAP show large zonal differences in estimating mean precipitation rate (e.g., exceeding 1 mm day2 1) in certain zones. In the tropics (between 368S and 368N) the difference between MCTA and GPCP is the smallest and the observed underestimation of GPCP compared to MCTA is likely caused by missing light precipitation in marine subsidence regions (e.g., Behrangi et al. 2012;
Significant differences between the products exist between 308 and 658S(Fig. 4a). This is important for both water and energy cycle studies as each 1 mm day21 of precipitation is equivalent to ;29 W m22 energy through latent heat release. The distinct local maximum precipitation around 408S shown by MCTA is not cap- tured by either GPCP or CMAP. Compared to MCTA, GPCP underestimates the mean zonal precipitation rate between ;308S and ;558S, but overestimates it between 558 and 658S. The local maximum around 608S is likely unrealistic. Comparing curves in Fig. 4 around 408, which is the edge of IR coverage in the GPCP, there is no obvious jump at 408, so the difference appears re- lated to the microwave algorithm used (microwave emission brightness temperature histograms; Chiu and Chokngamwong 2010). The disagreement between GPCP andMCTAaround608 [and higher latitudes in the North- ern Hemisphere (NH)] is driven by the TOVS/AIRS esti- mates (Adler et al. 2003). The TOVS/AIRS precipitation algorithm (Susskind and Pfaendtner 1989; Susskind et al. 1997) depends on regression of cloud volume against daily station data, with smoothing functions applied to obtain seasonally and latitudinally varying coefficients. The cloud volume-precipitation relation in the
The differences are less significant when precipitation rates are averaged globally or within a large zone (Table 1). The near-global (808S-808N) mean oceanic precipita- tion rate from MCTA is ;2.94 mm day21, which is about 4.17% (0.12mm day21) higher than that estimated by GPCP (2.82 mm day21) and about 9.25% (0.26 mm day21) higher than that obtained from CMAP (2.68 mm day21). For GPCP, the calculated relative differences are within the recent bias error estimate of about 7% (9%) for cli- matological precipitation over tropical (global) oceans (Adler et al. 2012).
Based on the 2C-SNOW-PROFILE product, the contribution of snowfall to the near-global oceanic precipitation rate is about 0.17 mm day21 (;5.8% of total precipitation; see Table 1). The MCTA estimate of mean precipitation rate (rain plus snow) in the NH is about 3.24 mm day21, with snowfall contributing about 0.12 mm day21 (;3.7% of total precipitation). In the SH, the mean precipitation rate from MCTA is 2.65 mm day21. In both hemispheres the MCTA estimate is higher than GPCP's or CMAP's. Snowfall in the SH is ;0.22 mm day21 (;8.3% of total precipitation), considerably larger than that in NH. Similar results are obtained using the CloudSat Liu08 snow product, as the two products show high agreement in capturing the zonal mean snowfall rate over ocean (Fig. 5). A maximum difference of about 0.2 mm day21 (;15% relative difference) is ob- served at approximately 608S. As the two products are independent of each other, it can be inferred that snow retrieval at this zone may have higher uncertainty than other zones.
4. Conclusions and final remarks
The more precise knowledge of precipitation amount and distribution improves our understanding of the current state of Earth's climate and the water and en- ergy budgets and how the hydrological cycle responds to the zonal energy imbalances that force climate change (Andrews et al. 2009). Therefore, it enhances our ability to understand how Earth's climate responds to in- creasing concentrations of greenhouse gases.
Complementary measurements from the CloudSat CPR, TRMM PR, and AMSR-E sensors offer a wide range of sensitivity to drizzle, light rainfall, snowfall, and intense precipitation that is not yet used in current merged precipitation products. Using these three advanced sen- sors and the merging technique described earlier, our es- timate for 3-yr (2007-09) near-global (808S-808N) oceanic mean precipitation rate is ;2.94 mm day21. This new estimate of mean global ocean precipitation is about 9% higher than that of CMAP (2.68 mm day21), mainly due to the lower estimate of CMAP at high latitudes. The MCTA estimate is only about 4% higher than that of GPCP (2.82 mm day21), but not as much as the ;15% considered recently to bring the surface energy budget into a balance (Stephens et al. 2012). We recognize that the uncertainty in global estimates of precipitation is itself subject to uncertainty, especially at higher lati- tudes, and is a topic of debate and ongoing research. MCTA, however, suggests distinct differences in the zonal distribution of precipitation rate from that de- picted in GPCP and CMAP, especially in the SH. Such zonal differences go beyond the known uncertainties and may exceed 1 mm day21 (or ;29 W m22 energy through latent heat release) in certain zones, especially at higher latitudes. However, the differences tend to cancel if precipitation is averaged over large portions of the globe.
Precipitation estimation at higher latitudes is clearly a major challenge that has not been fully addressed by the Earth observing community. While substantial sur- face observation of oceanic precipitation will remain impractical, our new estimate using the latest advances in remote sensing of precipitation can set a guideline for revising current climate data records of precipitation, surface energy balance, and evaluation of reanalyses and climate models. The upcoming Global Precipitation Measurement (GPM; Hou et al. 2008) mission will ex- tend the joint radar/passive microwave radiometer ca- pability pioneered by TRMM to retrieve precipitation of all phases to a latitude range of 658Nto658S, with significant improvements expected over land, and the future EarthCARE mission (Bezy et al. 2005) will ex- tend CloudSat-like observations. Efforts are underway to create seasonal climatology maps of oceanic pre- cipitation from MCTA, provide uncertainty data, and include the most recent datasets for the latest update. Furthermore, the authors are planning to explore avail- able ground validation resources including oceanic shipboard precipitation datasets (Petty 1997; Ellis et al. 2009; Andersson et al. 2011) and other sources such as atoll data and buoys for further evaluation and re- finement of the results. This work is also invaluable to improve future generation of the global precipitation climatology products.
Acknowledgments. The research described in this paper was carried out at the
_____, and Coauthors, 2003: The version-2
_____, G. Gu, and
Behrangi, A., K.-L. Hsu,B. Imam, S. Sorooshian,G. J.Huffman,and
Berg, W., T. L'Ecuyer, and
Bezy, J.-L., W. Leibrandt, A. Heliere,
Bolvin, D. T.,
Chiu, L. S., and R. Chokngamwong, 2010: Microwave emission brightness temperature histograms (METH) rain rates for climate studies: Remote Sensing Systems SSM/I version-6 results.
Ebert, E. E.,
Ellis, T. D., T. L'Ecuyer,
Haddad, Z. S.,
Hiley, M. J.,
Hou, A. Y.,
Huffman, G. J., and
_____, and Coauthors, 2007: The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38- 55, doi:10.1175/JHM560.1.
Iguchi, T., 2011: Evolution of the rain profiling algorithm for the TRMM Precipitation Radar. Proc. 2011 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS 2011),
_____, T. Kozu,
Joyce, R. J.,
Kiehl, J. T., and
Lebsock, M. D., and T. S. L'Ecuyer, 2011: The retrieval of warm rain from CloudSat. J. Geophys. Res., 116, D20209, doi:10.1029/ 2011JD016076.
Liu, G., 2008: Deriving snow cloud characteristics from CloudSat observations. J. Geophys. Res., 113, D00A09, doi:10.1029/ 2007JD009766.
Mitrescu, C., T. L'Ecuyer,
Petty, G. W., 1997: An intercomparison of oceanic precipitation frequencies from 10 Special Sensor Microwave/Imager rain rate algorithms and shipboard present weather reports. J. Geophys. Res., 102, 1757-1777, doi:10.1029/96JD03000.
Stephens, G. L., and Coauthors, 2008: CloudSat mission: Perfor- mance and early science after the first year of operation. J. Geophys. Res., 113, D00A18, doi:10.1029/2008JD009982.
_____, and Coauthors, 2010: Dreary state of precipitation in glo- bal models. J. Geophys. Res., 115, D24211, doi:10.1029/ 2010JD014532.
_____, and Coauthors, 2012: An update on Earth's energy balance in light of the latest global observations. Nat. Geosci., 5, 691-696, doi:10.1038/ngeo1580.
Susskind, J., and
Tian, Y., and
Trenberth, K. E.,
Xie, P., and
ALI BEHRANGI AND GRAEME STEPHENS
Earth System Science Interdisciplinary Center,
NASA Goddard Space Flight Center,
BJORN LAMBRIGTSEN AND MATTHEW LEBSOCK
Corresponding author address: Ali Behrangi,
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