THE STATUS OF THE NOAA/NESDIS OPERATIONAL AMSU PRECIPITATION ALGORITHM

THE STATUS OF THE NOAA/NESDIS OPERATIONAL AMSU PRECIPITATION ALGORITHM R.R. Ferraro NOAA/NESDIS Cooperative Institute for Climate Studies (CICS)/ESSIC 2207 Computer and Space Sciences Building Univerisity of Maryland, College Park, MD 20746 USA ABSTRACT The Advanced Microwave Sounding Unit (AMSU) which operates on the NOAA-15, -16 and -17 polar orbiting satellites generates a suite of operational hydrological cycle products through a system known as the Microwave Surface and Precipitation Products System (MSPPS). The MSPPS has been operational since January 1999. Through the use of the AMSU-B sensor, which contains five channels in the frequency range of 89 to 183 GHz with a nadir resolution of 16 km, precipitation rate retrievals are made using an advanced algorithm that performs a simultaneous retrieval of ice water path (IWP) and effective diameter size. The IWP is then converted to a precipitation rate using a cloud model derived relationship. In this paper, the status of this algorithm is presented. The paper begins with an overview of the SSM/I operational precipitation algorithm which served as a prototype for the AMSU algorithm. Then, the AMSU algorithm is described. Most recently (November 2003), a falling snow detection component to the land portion of the algorithm was added to expand the precipitation retrieval capability during winter seasons. The paper concludes with a discussion on future improvements to the algorithm as NOAA enters NOAA-N,N and METOP polar era, which contain the Microwave Humidity Sounder (MHS). 1. INTRODUCTION Scientists at the NOAA/NESDIS/Office of Research and Applications (ORA) have been involved in the development of rain rate retrieval algorithms for use on operational passive microwave (MW) satellite sensors for over 20 years. The early motivation was in the preparation for the NOAA- K,L,M Advanced Microwave Sounding Unit (AMSU), which was scheduled to fly in the late 1980 s but was not placed into operation until 1998. Work at NOAA with early research satellites, such as the Nimbus-5 & 6 ESMR (Electronically Scanning Microwave Radiometer) and the Nimbus-7 SMMR (Scanning Multichannel Microwave Radiometer), led to the development of the conceptual framework in which quantitative rain rates could be retrieved in an operational environment (Ferraro et al, 1986). As confidence grew with the ability of the MW sensors to accurately map rain areas and offer a qualitative assessment of the rain intensity, the NOAA/National Weather Service (NWS) began to request such products to support their operations and to fulfill several of NOAA s missions. These include advanced short term weather forecasts and warnings, and seasonal to interannual climate 1

monitoring. As such, the ORA has helped guide NOAA in the utilization of these rainfall products for a host of real-time applications. Although not an operational mission, the Tropical Rainfall Measurement Mission (TRMM) perhaps serves as the best paradigm for the current state of the art in routine rainfall retrieval from MW sensors through its TRMM TSDIS (TRMM Science Data and Information System). TRMM precipitation and radiance products, along with those from another research sensor, the EOS Aqua Advanced Scanning Microwave Radiometer (AMSR-E), have provided important information to NOAA meteorologists in their monitoring and forecasting of tropical cyclones. This paper briefly describes the evolution of operational MW derived rainfall retrieval algorithms and their application at NOAA, and presents some initial thoughts as to what the future has to offer. 2. SSM/I OPERATIONAL ALGORITHM The first Special Sensor Microwave Imager (SSM/I) instrument was launched on June 19, 1987 aboard the Defense Meteorological Satellite Program (DMSP) F-8 satellite. Other instruments have successfully operated on board the F-10 (December 1990), F-11 (November 1991), F-13 (April 1995), F-14 (September 1997) and F-15 (January 2000) satellites, the latter three of which are currently operational. The operational SSM/I algorithm was developed at NOAA/NESDIS (Grody 1991) and has been running at FNMOC for the past decade. It utilizes 85 GHz scattering information over land and 19 and 37 GHz emission plus 85 GHz scattering information over ocean to detect areas of rainfall. The conversion to rain rate was developed using global radar and SSM/I matchups (Ferraro and Marks, 1995). The algorithm utilizes discriminant functions to filter out false signatures due to snow cover, deserts and sea-ice cover (Ferraro 1997). Although this algorithm has been virtually unchanged for the past decade, it still remains widely used within NOAA for a variety of applications including short-term rainfall potential assessment, NWP model data assimilation, and global rainfall monitoring. Additionally, the Global Precipitation Climatology Project (GPCP) uses this algorithm (Huffman et al 1996) (Fig. 1). The SSM/I is being replaced by an advanced sensor, the Special Sensor Microwave Imager Sounder (SSMIS) that was first placed into orbit on the F-16 satellite (October 2004) which is undergoing final instrument checkout. The set of window channels on the SSMIS that are used for precipitation retrieval are very similar to those on the SSM/I. Figure 1 SSM/I derived global annual rainfall (mm/year) based on data from July 1987 - July 2004. 2

3. AMSU OPERATIONAL ALGORITHM The first Advanced Microwave Sounding Unit (AMSU) was placed into operation on the NOAA-15 satellite during July 1998. The AMSU sensor package consists of the AMSU-A (15 channels spanning 23 to 89 GHz with a nadir FOV of 45 km) and AMSU-B (5 channels between 89 to 183 GHz with a nadir FOV of 15 km). Subsequent AMSU s were launched and are operational on board the NOAA-16 (September 2000) and NOAA-17 (June 2002) satellites. This three-satellite constellation offers global observations approximately every four hours. Although the primary purpose of the AMSU is for temperature (AMSU-A) and moisture sounding (AMSU-B), the availability of window channels offers the opportunity to derive surface and atmospheric parameters comparable to the SSM/I. In fact, the heritage of the SSM/I algorithms were used to develop the original AMSU operational algorithm, even though there are significant differences between the two sensors (Table 1). Characteristic AMSU SSM/I Primary Window Channels 23.8, 31.4,50.3,89, 150GHz 19.4, 22.2, 37, 85.5 GHz Polarization Mixed V and H Scan Geometry Cross Track: 0 48 degrees Conical: Fixed 45 degrees FOV properties Vary with view angle Fixed with frequency Fixed across scan Vary with frequency Swath Width ~2200 km ~1400 km Table 1. Comparison of AMSU and SSM/I sensors. It has been demonstrated that the precipitation signature at frequencies at or above 150 GHz are more sensitive to the scattering due to precipitation layer ice compared to measurements at frequencies at or below 90 GHz (Zhao and Weng, 2002; Bennartz and Bauer, 2003; Chen and Staelin, 2003; Ferraro et al., 2000; Weng et al., 2003). With the inclusion of measurements on the AMSU-B module at sufficient spatial resolution, new algorithms were developed for the retrieval of ice water path (IWP) and particle effective size (D e ), and precipitation rate. The current NOAA/NESDIS set of algorithms (Weng et al. 2003; Ferraro et al., 2005) are detailed below. The ice cloud scattering parameter, Ω, is defined as Ω = (1 - ωg) τ / 2µ (1) Here, τ is the ice cloud optical thickness, ω is the cloud single-scattering albedo, g is the asymmetry factor and µ is the cosine of the zenith angle. By assuming that the ice particles follow a gamma distribution, Ω is calculated using Mie theory and can be expressed in terms of the IWP, D e and the ice particle bulk volume density (ρ) Ω = IWP Ω N / µρd e (2) where Ω N is a normalized scattering parameter, dependent upon the particle effective size and complex index of refraction. From (2), it is apparent that the variation of the scattering parameter results from changes in the cloud ice water path and particle size. Through the use of a twostream approximation, the ice cloud scattering parameter can also be derived by using the AMSU- B measurements at 89 and 150 GHz. Finally, assuming a modified Gamma size distribution and a constant ice particle bulk volume density, the regression relationships of De - r and Ω N - r are obtained as follows: D e = a 0 + a 1 r+ a 2 r 2 + a 3 r 3 (3) 3

Ω N = exp (b 0 + b 1 ln(d e )+ b 2 ln(d e ) 2 ), (4) where r = Ω 89 / Ω 150 is the scattering parameter ratio; a i (i=0,1,2,3) and b i (i=0,1,2) are the coefficients that are dependent on the ice particle bulk density and size distribution. For a smaller D e, Ω N at 150 GHz is significantly higher than at 89 GHz; however, as D e approaches 2 mm, Ω N approaches the same value. Given ρ, IWP and D e can be uniquely determined from (2) through (4) using the AMSU-B measurements at 89 and 150 GHz. Recent improvements to this algorithm include a two-stream correction of the brightness temperature (TB) at 89 and 150 GHz as a function of µ. In addition, two sets of values for the a i and b i coefficients have been employed based upon the value of D e and are presented in Table 2. a 0 a 1 a 2 a 3 Particle Effective -0.300323 4.30881-3.98255 2.78323 Diameter b 0 b 1 b 2 D e <1 mm -0.294459 1.38838-0.753624 D e >1 mm -1.19301 2.08831-0.857469 Table 2. Coefficients a and b that are used in the retrieval of IWP through equations (3) and (4) for two different conditions of D e. For retrieving rain rate (RR), the IWP is converted into the surface rainfall rate using the cloud model results (Weng et al. 2003; Ferraro et al., 2005). The relationship takes the form: RR = r 0 + r 1 IWP + r 2 IWP 2 (5) In most precipitation systems, the rain layer extends above the freezing level and contains a mixture of water and ice particles. Scattering of the upwelling radiation due to millimeter sized ice particles occurs within the precipitation layer, with the higher frequencies being affected more by the smaller particles. Depending upon the cloud microphysics and vertical velocities, the size and density of these ice particles will vary. An approach is made to utilize the AMSU 183 GHz moisture sounding channels to classify different rain types. It was found that the three sounding channels at 183 +1, +3, +7 GHz which are sensitive to the water vapor at different atmospheric levels also provide unique signals on the vertical extent of frozen hydrometeors. In particular, the brightness temperature at 183 +7 GHz is sensitive to the presence of ice particles at lower altitudes, whereas the measurements at 183 +1 GHz primarily respond to deep convection where large ice particles are thrust substantially higher into upper atmosphere (Bennartz and Bauer, 2003; Chen and Staelin, 2003). An indicator of the convective strength, or index, (CI) of cloud systems is defined and calculated based on the information inferred from the AMSU 183 GHz measurements. Specifically, CI is defined as a series of brightness temperature differences: 1 = TB 183+1 - TB 183+7 2 = TB 183+3 - TB 183+7 3 = TB 183+1 - TB 183+3 (6a) (6b) (6c) where CI=1 (an indicator of weak convection or stratiform rain), CI=2 (an indicator of moderate convection) and CI=3 (an indicator of strong convection), and CI=1 when 2 > 0, 2 > 1 and 2 > 3 (7a) 4

CI =2 when 1 > 0, 2 > 0, 3 > 0, 1 > 2, 1 > 3, and 2 > 3 CI=3 when 1 > 0, 2 > 0, 3 > 0, 1 > 2, 1 > 3, and 2 < 3. (7b) (7c) This classification scheme is integrated as part of the current AMSU precipitation algorithm, where the coefficients r i in (5) are altered based on the CI value given in (7). Presently, two sets of coefficients are used: For CI=1 or 2, RR = 0.322 + 16.504 IWP - 3.342 IWP 2 (8a) For CI=3, RR = 0.089 + 20.819 IWP - 2.912 IWP 2 (8b) The maximum allowed rain rate is 30 mm hr -1. It should be noted that the algorithm employs a set of discriminant functions to filter out potential false signatures (Ferraro et al, 2005). This new algorithm performs well under a wide range of precipitation systems, an example of which is presented in Figure 2. Real-time imagery of this product is available from several web sites, including http://www.orbit.nesdis.noaa.gov/corp/scsb/mspps/main.html. Additionally, under the guidance of the IPWG, the product is routinely monitored and validated against surface rain gauges and radar estimates over the United States (http://www.cpc.ncep.noaa.gov/products/janowiak/us_web.shtml) and Australia (http://www.bom.gov.au/bmrc/wefor/staff/eee/satrainval/dailyval_dev.html). The retrieval of cold season precipitation over land is a never-ending challenge for passive microwave remote sensing. This is because of the non-uniqueness of the scattering signature associated with precipitation and other surface features such as snow cover and deserts. The retrieval is even more complex when the precipitation is in the form of snow, as the scattering signal becomes weaker and the underlying surface is generally snow covered. Nonetheless, for many parts of the world, snowfall is the primary source of precipitation, and with sparse surface reports, satellite measurements offer the best opportunity to gather information on falling snow. 5

Figure 2. AMSU derived rain rates for Hurricane Ivan at 1939 UTC on 15 September 2004. Units are in inches per hour. Recently, an expansion to the AMSU algorithm to include the detection of precipitation of snow covered and cold surfaces was developed (Kongoli et al, 2003). It utilizes additional measurements from AMSU-A at 53.6 GHz and three bands on AMSU-B that surround the 183 GHz water vapor absorption region which expands the land retrievals to include those from falling snow. Previous to this, all cold surfaces (e.g., snow cover and colder than 269 K) were flagged as indeterminate. This extension to the precipitation algorithm was implemented at NOAA/NESDIS in November 2003 and work is in progress to improve this algorithm and to add in a snowfall rate. Figure 3 shows an example of this product. Note that there are still large areas of indeterminate precipitation, as these are regions where the algorithm cannot be applied due to cold and dry atmospheric conditions that would result in false signatures due to surface snow. Recent work suggests that the algorithm can be extended to slightly colder situations; however, not all snowfall can be detected simply due to the shallow nature of some precipitation systems. Nonetheless, this initial AMSU algorithm shows significant promise in the detection of snowfall over land. 6

Figure 3. (Left) NOAA-15 AMSU derived precipitation estimates between 1100 and 1500 UTC for 25 January 2004. Rain rates are indicated by the various colors while falling snow is denoted in blue. Precipitation free areas are colored pink while indeterminate regions are in gray. (Right) NCEP surface weather for 1200 UTC 25 January 2004. 4. CONSOLIDATION OF ALGORITHMS The current era of MW rain retrievals is such that even so called research missions are becoming an integral part of satellite data used by operational agencies. These data are critical to fill voids in the current operational satellite series. In support of the AMSR-E and TRMM missions ORA has been working closely with NASA on improving the Goddard Profiling Algorithm (GPROF) as a first step in developing a unified retrieval algorithm that could then be used on multiple sensors (Kummerow et al., 2001; McCollum and Ferraro, 2003; Wilheit et al., 2003). At present, GPROF V6 is being used within the TRMM 2A12 and AMSR-E precipitation algorithms. In addition, NESDIS has recently implemented a near real-time capability for AMSR-E that was developed under the auspices of the Joint Center for Satellite Data Assimilation (JCSDA). Ultimately, the GPROF algorithm will be expanded to include a larger frequency range so that AMSU type sensors can be included and the retrievals can be made over a wider range of surface conditions, including snow covered land. Additionally, error models are being developed so that retrievals will include a corresponding uncertainty estimate. 5. FUTURE NOAA is consolidating the DMSP and NOAA polar programs into the NPOESS (National Polarorbiting Operational Environmental Satellite System) ready for launch in around 2008. It will contain the most advanced MW radiometer flown to date: the CMIS (Conical Microwave Imager/Sounder). Prior to that, NOAA and EUMETSAT will initiate a joint polar satellite series (IJPS) with the launch of NOAA-N, N and METOP-1 and -2, which will contain an AMSU-A and MHS (Microwave Humidity Sounder), similar to AMSU-B. Additionally, the DMSP will fly 4 more SSMIS sensors bridging the gap into the NPOESS era. So, the current heritage of SSM/I and AMSU precipitation algorithms will carry into the NPOESS timeframe. However, it is envisioned that an algorithm like GPROF will eventually be implemented for operational use for SSMIS and NPOESS and utilize all available measurements between 19 and 183 GHz. 7

The NASA sponsored Global Precipitation Measurement (GPM) program is scheduled for launch around 2010 and it will consist of all available polar orbiting satellites (both research and operational), supplemented with a core satellite that will contain a dual-polarization radar along with an advance microwave radiometer to achieve a 3-hour global coverage of precipitation. GPM, although a research mission, will serve as the prototype for an international operational precipitation-monitoring mission. It includes partners from Japan, Europe, South Korea and India. NOAA has already been working closely with NASA on early GPM research and development and plans for a formal partnership are inevitable. Ultimately, NOAA will combine measurements from the GPM satellite constellation with visible and infrared measurements from the GOES-R satellite to develop state-of-the-art retrieval schemes to meet its wide array of precipitation needs. The views, opinions, and findings contained in this report are those of the author(s) and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision. 6. REFERENCES Bennartz, R. and P. Bauer, 2003: Sensitivity of microwave radiances at 85-183 GHz to precipitating ice particles, Radio Science, 38, 8075-8090. Chen, F.W. and D.H. Staelin, 2003: AIRS/AMSU/HSB precipitation characteristics, IEEE Trans. Geosci. Rem. Sens., 41, 410-417. Ferraro, R.R., N.C. Grody and J.A. Kogut, 1986: Classification of geophysical parameters using passive microwave satellite measurements. IEEE Trans. Geo. Rem. Sens., 24, pp 1008-1013. Ferraro, R.R. and G.F. Marks, 1995: The development of SSM/I rain rate retrieval algorithms using ground based radar measurements. J. Atmos. Oceanic Tech., 12, 755-770. Ferraro, R.R., 1997: SSM/I derived global rainfall estimates for climatological applications. J. Geophys. Res., 102, pp 16,715-16,735. Ferraro, R.R., F. Weng, N.C. Grody and L. Zhao, 2000: Precipitation characteristics over land from the NOAA-15 AMSU Sensor, Geophys. Res. Letters, 27, 2669-2672. Ferraro, R.R., F. Weng, N.C. Grody, L. Zhao, H. Meng, C. Kongoli, P. Pellegrino, S. Qiu and C. Dean, 2005: NOAA operational hydrolocial products derived from the Advanced Microwave Sounding Unit (AMSU), In press, IEEE Trans. Geosci. Rem. Sens. Grody, N.C., 1991: Classification of snow cover and precipitation using the Special Sensor Microwave Imager. J. Geophys. Res., 96, pp 7423-7435. Huffman, G.J., R.F. Adler, P. Arkin, A. Chang, R.R. Ferraro, A.Gruber, J.Janowiak, A. McNab, B. Rudolf and U. Schneider, 1996: The Global Precipitation Climatology Project (GPCP) combined precipitation data set, Bull. of Amer. Meteor. Soc., 78, pp 5-20. Kongoli, C., P. Pellegrino and R. Ferraro, 2003: A new snowfall detection algorithm over land using measurements from the AMSU, Geophys. Res. Let., 30, 1756-1759. 8

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