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Global Precipitation Means and Variations with the New Version of GPCP

Knowledge of global precipitation means, patterns and variations is essential for understanding the global water cycle. The observation-based global analysis of the Global Precipitation Climatology Project (GPCP) has been a key input to many such studies, including those related to means of the global (and regional) water and energy cycles. A new version (Version 3.1) of the GPCP monthly analysis is now available (1983-2019), with finer spatial resolution (0.5º latitude/longitude), updated satellite algorithms, latest gauge analysis over land from the Global Precipitation Climatology Center (GPCC), and with ocean climatologies adjusted using information from the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) mission and CloudSat. The presentation will give key findings from the new analysis, compare with the previous version, link to studies of the water cycle and compare to CMIP6 climate model results. The GPCP Monthly V3.1 analysis uses a Tropical Composite Climatology (TCC) using 22 years (1998-2019) of TRMM and GPM-based surface precipitation estimates from passive microwave, radar and combined passive microwave and radar observations to adjust the long-term mean values in the tropics over ocean. At higher latitudes over ocean climatological values of merged CloudSat and GPM combined passive microwave and radar surface precipitation estimates are used. The result of the new algorithms and procedures is an ocean mean value 60º N to 60º S of 3.2 mm/d, an increase of 7% from the older V2.3. Over land the gauge analysis (from GPCC in Germany), combined with satellite estimates results in a small decrease (~ 0.5%) from the previous version, giving a total global precipitation mean of 2.81 mm/d for V3.1, an increase of 4.5%. Variations from inter-annual to trend scales over most of the ocean are driven by the Colorado State University (CSU) Goddard Profiling (GPROF) algorithm applied to SSMI/SSMIS satellite data. The new GPCP version retains a near zero trend of global precipitation, with significant positive trends in the deep tropics along the Pacific ITCZ and elsewhere, countered by middle latitude decreases, very similar to the previous version of the GPCP Monthly analysis. The pattern of trends is similar to that of AMIP climate model results, driven by observed SSTs for the period in question, but very different from results for “free-running” CMIP historical ensembles, likely due in part to the relatively short comparison period and effects of inter-decadal variations in the GPCP and AMIP results that are not in the “history” models. Interannual variations are also nearly the same in the new version, but with finer detail, although ENSO variations over the ocean have slightly larger amplitude than before, an effect likely related to the change in ocean satellite algorithm used in Version 3. For precipitation intensity (percentiles) at the monthly scale, especially in the tropics, GPCP shows a positive trend for the upper one third of the percentiles (Pct ≥ 70th) and a much weaker positive trend for the lowest percentiles (Pct ≤ 10th), while negative trends appear for the middle one-half percentiles (20th-65th). AMIP results agree with those from the GPCP in terms of the sign of the changes/trends for high and intermediate percentiles. The CMIP historical results also agree in the sign of the trends, but the trends are weaker. Comparisons with other types of CMIP historical simulations including the GHG-only, aerosol-only, and nature-only runs suggest that the observed changes/trends in precipitation amount and intensity during the GPCP period are dominated by a combination of the effects of the Pacific Decadal Oscillation (PDO) and anthropogenic GHG-related surface warming.

Robert Adler↗
NASA NTRS2013

The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series

Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981-2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods-the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT-revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50-60% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction.

LAI↗
NASA NTRS1981

Comparison of observed and computed brightness temperatures for the HIRS2 and MSU sounders on TIROS-N

Given the radiosonde temperature and humidity profile, brightness temperatures for the temperature sounding IR channels on HIRS 2 were calculated. The temperature profile was interpolated to 53 levels between 1000 and 30 mb assuming temperature to be linear in the logarithm of the pressure between the mandatory levels. The 11 levels above 30 mb were extrapolated according to climatology. The surface was taken to be the climatological sea surface temperature. The specific humidity was interpolated between mandatory levels assuming a p(n) dependence. A specific humidity corresponding to a climatological water vapor mixing ratio of 2 ppmv was assumed at and above 100 mb. Zonally averaged climatological ozone profiles were used to compute the ozone component of the transmittance.

Susskind, J.↗
NASA NTRS1992

Effects of clouds on the Earth radiation budget; Seasonal and inter-annual patterns

Seasonal and regional variations of clouds and their effects on the climatological parameters were studied. The climatological parameters surface temperature, solar insulation, short-wave absorbed, long wave emitted, and net radiation were considered. The data of climatological parameters consisted of about 20 parameters of Earth radiation budget and clouds of 2070 target areas which covered the globe. It consisted of daily and monthly averages of each parameter for each target area for the period, Jun. 1979 - May 1980. Cloud forcing and black body temperature at the top of the atmosphere were calculated. Interactions of clouds, cloud forcing, black body temperature, and the climatological parameters were investigated and analyzed.

Dhuria, Harbans L.↗
NASA NTRS1993

The effects of orbital and climatic variations on Martian surface heat flow

Large changes in the orbital elements of Mars on timescales of 10(exp 4) to 10(exp 6) years will cause widely varying climate, specifically surface temperatures, as a result of varying insolation. These surface temperature oscillations will produce subsurface thermal gradients which contribute to the total surface heat flux. We investigate the thermal behavior of the Martian regolith on orbital timescales and show that this climatological surface heat flux is spatially variable and contributes significantly to the total surface heat flux at many locations. We model the thermal behavior of the Martian regolith by calculating the mean annual surface temperatures for each epoch (spaced 1000 years apart to resolve orbital variations) for the past 200,000 years at a chosen location on the surface. These temperatures are used as a boundary condition for the deeper regolith and subsurface temperature oscillation are then computed. The surface climatological heat flux due to past climate changes can then be found from the temperature gradient between the surface and about 150 m depth (a fraction of the thermal skin depth on these timescales). This method provides a fairly accurate determination of the climatological heat flow component at a point; however, this method is computationally time consuming and cannot be applied to all points on the globe. To map the spatial variations in the surface heat flow we recognize that the subsurface temperature structure will be largely dominated by the most recent surface temperature oscillations. In fact, the climate component of the surface heat flow will be approximately proportional to the magnitude of the most recent surface temperature change. By calculating surface temperatures at all points globally for the present epoch and an appropriate past epoch, and combining these results with a series of more precise calculations described above, we estimate the global distribution of climatological surface heat flow.

Mellon, Michael T.↗
NASA NTRS1993

Comparison of ISCCP and other cloud amounts

A new 8-year global cloud climatology has been produced by the International Satellite Cloud Climatology Project (ISCCP) that provides information every 3 h at 280-km spatial resolution covering the period from July 1983 through June 1991. If cloud detection errors and differences in area sampling are neglected, individual ISCCP cloud amounts agree with individual surface observations to within 15% rms with biases of only a few percent. When measurements of small-scale, broken clouds are isolated in the comparison, the rms differences between satellite and surface cloud amounts are about 25%, similar to the rms difference between ISCCP and Landsat determinations of cloud amount. For broken clouds, the average ISCCP cloud amounts are about 5% smaller than estimated by surface observers (difference between earth cover and sky cover), but about 5% larger than estimated from very high spatial resolution satellite observations (overestimate due to low spatial resolution offset by underestimate due to finite radiance thresholds). Detection errors caused by errors in the ISCCP clear-sky radiances or incorrect radiance threshold magnitudes, are the dominant source of error in monthly average cloud amounts. The ISCCP cloud amounts appear to be too low over land by about 10%, somewhat less in summer and somewhat more in winter, and about right (maybe slightly low) over oceans. In polar regions, ISCCP cloud amounts are probably too low by about 15%-25% in summer and 5%-10% in winter. Comparison of the ISCCP climatology to three other cloud climatologies shows excellent agreement in the geographic distribution and seasonal variation of cloud amounts; there is little agreement about day/night contrasts in cloud amount. Notable results from ISCCP are that the gobal annual mean cloud amount is about 63%, being about 23% higher over oceans than over land, that it varies by less than 1% rms from month to month, and that it has varied by about 4% on a time scale approximately equal to 2-4 years. The magnitude of interannual variations of local (280-km scale) monthly mean cloud amounts is about 7%-9%. Longitudinal contrasts in cloud amount are just as large as latitudinal contrasts. The largest seasonal variation of cloud amount occurs in the tropics, being larger in summer than in winter; the seasonal variation in the middle latitudes has the opposite phase. Polar regions may have little seasonable variability in cloud amount. The ISCCP results show slightly more nightime than daytime cloud amount over oceans and more daytime than nightime cloud amount over land.

Rossow, William B.↗
NASA NTRS1994

Temperature minima in the average thermal structure of the middle mesosphere (70 - 80 km) from analysis of 40- to 92-km SME global temperature profiles

Global temperatures have been derived for the upper stratosphere and mesosphere from analysis of Solar Mesosphere Explorer (SME) limb radiance profiles. The SME temperature represent fixed local time observations at 1400 - 1500 LT, with partial zonal coverage of 3 - 5 longitudes per day over the 1982-1986 period. These new SME temperatures are compared to the COSPAR International Ionosphere Reference Atmosphere 86 (CIRA 86) climatology (Fleming et al., 1990) as well as stratospheric and mesospheric sounder (SAMS); Barnett and Corney, 1984), National Meteorological Center (NMC); (Gelman et al., 1986), and individual lidar and rocket observations. Significant areas of disagreement between the SME and CIRA 86 mesospheric temperatures are 10 K warmer SME temperatures at altitudes above 80 km. The 1981-1982 SAMS temperatures are in much closer agreement with the SME temperatures between 40 and 75 km. Although much of the SME-CIRA 86 disagreement probably stems from the poor vertical resolution of the observations comprising the CIRA 86 modelm, some portion of the differences may reflect 5- to 10-year temporal variations in mesospheric temperatures. The CIRA 86 climatology is based on 1973-1978 measurements. Relatively large (1 K/yr) 5- to 10-year trends in temperatures as functions of longitude, latitude, and altitude have been observed for both the upper stratosphere (Clancy and Rusch, 1989a) and mesosphere (Clancy and Rusch, 1989b; Hauchecorne et al., 1991). The SME temperatures also exhibit enhanced amplitudes for the semiannual oscillation (SAO) of upper mesospheric temperatures at low latitudes, which are not evident in the CIRA 86 climatology. The so-called mesospheric `temperature inversions' at wintertime midlatitudes, which have been observed by ground-based lidar (Hauschecorne et al., 1987) and rocket in situ measurements (Schmidlin, 1976), are shown to be a climatological aspect of the mesosphere, based on the SME observations.

Clancy, R. Todd↗
NASA NTRS1998

Empirical correction for earth sensor horizon radiance variation

A major limitation on the use of infrared horizon sensors for attitude determination is the variability of the height of the infrared Earth horizon. This variation includes a climatological component and a stochastic component of approximately equal importance. The climatological component shows regular variation with season and latitude. Models based on historical measurements have been used to compensate for these systematic changes. The stochastic component is analogous to tropospheric weather. It can cause extreme, localized changes that for a period of days, overwhelm the climatological variation. An algorithm has been developed to compensate partially for the climatological variation of horizon height and at least to mitigate the stochastic variation. This method uses attitude and horizon sensor data from spacecraft to update a horizon height history as a function of latitude. For spacecraft that depend on horizon sensors for their attitudes (such as the Total Ozone Mapping Spectrometer-Earth Probe-TOMS-EP) a batch least squares attitude determination system is used. It is assumed that minimizing the average sensor residual throughout a full orbit of data results in attitudes that are nearly independent of local horizon height variations. The method depends on the additional assumption that the mean horizon height over all latitudes is approximately independent of season. Using these assumptions, the method yields the latitude dependent portion of local horizon height variations. This paper describes the algorithm used to generate an empirical horizon height. Ideally, an international horizon height database could be established that would rapidly merge data from various spacecraft to provide timely corrections that could be used by all.

Hashmall, Joseph A.↗
NASA NTRS1998

Detection of Polar Stratospheric Clouds Using AVHRR Imagery

In recent years, the role of PSCs in the ozone depletion process has become better understood. PSCs provide the surfaces upon which heterogeneous reactions take place that affect the gas phase partitioning between active and reservoir chlorine and nitrogen species. Present methods of PSC detection include in situ measurements by lidar and various satellite-borne instruments such as the Stratospheric Aerosol Measurement II (SAM II) on the Nimbus 7 spacecraft, which produced PSC measurements from 1978 to 1994 and several instruments onboard the Upper Atmospheric Research Satellite (UARS) such as the Cryogenic Limb Array Etalon Spectrometer (CLAES) which provided measurements for 1991-1993. All of the PSC-detection methods devised so far have been hampered by incomplete sampling of the places and times in which PSCs are likely to form. There is a need to understand the climatology of PSCS, in particular the timing of their onset and duration, their vertical distribution, geographic extent, annual variability and responses to volcanic aerosol forcing. Poole and Pitts [1994] assembled a PSC climatology based on SAM II data, but this climatology is incomplete, as it is limited to the edge of the polar night due to the limitations of the solar occultation scan geometry. The Advanced Very High Resolution Radiometer (AVHRR) five- channel sensors onboard the NOAA polar-orbiting satellites have been collecting data over the polar regions continuously since 1979. These operational satellites provide unmatched coverage in space and time of both polar regions, but were not designed for the detection of optically-thin PSCS. However, the AVHRR data archive would be an invaluable source for the construction of a long-term climatology of PSCs if techniques can be developed and tested to detect PSCs in AVHRR data. In the last few years, the members of our group at San Francisco State University and NASA Ames Research Center have been engaged in the development of various PSC detection methods using AVHRR data. There is strong evidence that a subset of PSCS, those that are optically thick, can be readily identified in the AVHRR data set. Our group has also made significant progress in the identification of optically thinner PSCs using a variety of techniques.

Garcia, Oswaldo↗
NASA NTRS1999

Constructing Synoptic Maps of Stratospheric Column Ozone from HALOE, SAGE and Balloonsonde Data Using Potential Vorticity Isentropic Coordinate Transformations

In this study we utilize potential vorticity - isentropic (PVI) coordinate transformations as a means of combining ozone data from different sources to construct daily, synthetic three-dimensional ozone fields. This methodology has been used successfully to reconstruct ozone maps in particular regions from aircraft data over the period of the aircraft campaign. We expand this method to create high-resolution daily global maps of profile ozone data, particularly in the lower stratosphere, where high-resolution ozone data are sparse. Ozone climatologies in PVI-space are constructed from satellite-based SAGE II and UARS/HALOE data, both of which-use solar occultation techniques to make high vertical resolution ozone profile measurements, but with low spatial resolution. A climatology from ground-based balloonsonde data is also created. The climatologies are used to establish the relationship between ozone and dynamical variability, which is defined by the potential vorticity (in the form of equivalent latitude) and potential temperature fields. Once a PVI climatology has been created from data taken by one or more instruments, high-resolution daily profile ozone field estimates are constructed based solely on the PVI fields, which are available on a daily basis from NCEP analysis. These profile ozone maps could be used for a variety of applications, including use in conjunction with total ozone maps to create a daily tropospheric ozone product, as input to forecast models, or as a tool for validating independent ozone measurements when correlative data are not available. This technique is limited to regions where the ozone is a long-term tracer and the flow is adiabatic. We evaluate the internal consistency of the technique by transforming the ozone back to physical space and comparing to the original profiles. Biases in the long-term average of the differences are used to identify regions where the technique is consistently introducing errors. Initial results show the technique is useful in the lower stratosphere at most latitudes throughout the year,and in the winter hemisphere in the middle stratosphere. The results are problematic in the summer hemisphere middle stratosphere due to increased ozone photochemistry and weak PV gradients. Alternate techniques in these regions will be discussed. An additional limitation is the quality and resolution of the meteorological data.

Hollandsworth, Stacey M.↗
NASA NTRS2001

Implications of the Observed Mesoscale Variations of Clouds for Earth's Radiation Budget

The effect of small-spatial-scale cloud variations on radiative transfer in cloudy atmospheres currently receives a lot of research attention, but the available studies are not very clear about which spatial scales are important and report a very large range of estimates of the magnitude of the effects. Also, there have been no systematic investigations of how to measure and represent these cloud variations. We exploit the cloud climatology produced by the International Satellite Cloud Climatology Project (ISCCP) to: (1) define and test different methods of representing cloud variation statistics, (2) investigate the range of spatial scales that should be included, (3) characterize cloud variations over a range of space and time scales covering mesoscale (30 - 300 km, 3-12 hr) into part of the lower part of the synoptic scale (300 - 3000 km, 1-30 days), (4) obtain a climatology of the optical thickness, emissivity and cloud top temperature variability of clouds that can be used in weather and climate GCMS, together with the parameterization proposed by Cairns et al. (1999), to account for the effects of small-scale cloud variations on radiative fluxes, and (5) evaluate the effect of observed cloud variations on Earth's radiation budget. These results lead to the formulation of a revised conceptual model of clouds for use in radiative transfer calculations in GCMS. The complete variability climatology can be obtained from the ISCCP Web site at http://isccp.giss.nasa.gov.

Rossow, William B.↗
NASA NTRS2002

Configuration and Intraseasonal Duration of Interannual Anomalies of the Great Plains Low-Level Jet

Despite the fact that the low-level jet of the southern Great Plains (the GPLLJ) of the U.S. is primarily a nocturnal phenomenon that virtually vanishes during the daylight hours, it is one of the most persistent and stable climatological features of the low-level continental flow during the warm-season months, May through August. We have used significant-level data to validate the skill of the GEOS-1 Data Assimilation System (DAS) in realistically detecting this jet and inferring its structure and evolution. We have then carried out a 15-year reanalysis with the GEOS-1 DAS to determine its climatology and mean diurnal cycle and to study its interannual variability. Interannual anomalies of the meridional flow associated with the GPLLJ are much smaller than the mean diurnal fluctuations, than random intraseasonal anomalies, and than the mean wind itself. There are three maxima of low-level meridional flow variance over the Great Plains and the Gulf of Mexico: a 1.2 m2 s-2 peak over the southeast Texas, to the east and south of the mean velocity peak, a 1.0 m2 s-2 peak over the western Gulf of Mexico, and a .8 m2 s-2 peak over the upper Great Plains (UGP), near the Nebraska/South Dakota border. Each of the three variance maxima corresponds to a spatially coherent, jet-like pattern of low-level flow interannual variability. There are also three dominant modes of interannual variability corresponding to the three variance maxima, but not in a simple one-to-one relationship. Cross-sectional profiles of mean southerly wind over Texas remain relatively stable and recognizable from year to year with only its eastward flank showing significant variability. This variability, however, exhibits a distinct, biennial oscillation during the first six to seven years of the reanalysis period and only then. This intermittent biennial oscillation (IBO, one of the three modes discussed in the previous paragraph) in the lowlevel flow is restricted to the region surrounding eastern Texas and is also evident in the NCEP/NCAR reanalysis data set from about 1978 to 1985 or 1986 and again from 1995 to 2000. It is evident as well in surface pressure in both the GEOS-1 and NCEP/NCAR sets. The interannual anomalies do not necessarily persist uniformly throughout an entire season, but can fluctuate from one part of the season to the next. To estimate the characteristic sub-seasonal time scales for coherence of these fluctuations, we have taken the weekly anomaly of low-level wind at each point of the domain from the climatological average for that given point and that given week of the season and computed the covariance of its fluctuations over all weeks and over all years with the weekly climatological anomaly of the meridional wind at each of the three reference points discussed above. The typical duration of a coherent interannual anomaly within a given warm season increases with decreasing latitude from 2 to 3 weeks over the UGP, to 6 to 7 weeks over eastern Texas. Coherence over the western Gulf of Mexico is intermediate between the two with a typical duration of 4 to 5 weeks. There appears to be evidence that the interannual anomalies over Texas the Gulf propagate to the UGP after a week and those over the Gulf propagate there after 2 to 3 weeks. There also appears to be some reverse propagation of interannual anomalies over the UGP to Texas and to the Gulf after a period of about one week. The interannual anomalies in southerly flow over eastern Texas seem to correlate well with interannual anomalies of surface temperature and (negative) ground wetness and over western Texas.

Helfand, H. M.↗
NASA NTRS2004

Earth GRAM-99 and Trace Constituents

Global Reference Atmospheric Model (GRAM-99) is an engineering-level model of Earth's atmosphere. It provides both mean values and perturbations for density, temperature, pressure, and winds, as well as monthly- and geographically-varying trace constituent concentrations. From 0-27 km, GRAM thermodynamics and winds are based on National Oceanic and Atmospheric Administration Global Upper Air Climatic Atlas (GUACA) climatology. Above 120 km, GRAM is based on the NASA Marshall Engineering Thermosphere (MET) model. In the intervening altitude region, GRAM is based on Middle Atmosphere Program (MAP) climatology that also forms the basis of the 1986 COSPAR International Reference Atmosphere (CIRA). Atmospheric composition is represented in GRAM by concentrations of both major and minor species. Above 120 km, MET provides concentration values for N2, O2, Ar, O, He, and H. Below 120 km, species represented also include H2O, O3, N2O, CO, CH4, and CO2. At COSPAR 2002 a comparison was made between GRAM constituents below 120 km and those provided by Naval Research Laboratory (NRL) climatology. No current need to update GRAM constituent climatology in that height range was identified. This report examines GRAM (MET) constituents between 100 and 1000 km altitudes. Discrepancies are noted between GRAM (MET) constituent number densities and mass density or molecular weight. Near 110 km altitude, there is up to about 25% discrepancy between MET number density and mass density (with mass density being valid and number densities requiring adjustment). Near 700 km altitude there is also up to about 25% discrepancy between MET number density and mean molecular weight (with molecular weight requiring adjustment). In neither case are MET mass density estimates invalidated. These discrepancies have been traced to MET subroutines SLV (which affects 90-170 km height range) and SLVH (which affects helium above 440 km altitude). With these discrepancies corrected, results are presented to illustrate GRAM (MET) constituent mole fractions in terms of height-latitude cross sections from 100 to 1000 km altitude, and latitude-longitude 'maps' at 450 km (approximate height of International Space Station). Plans are discussed for an update of MET (and GRAM) to correct these constituent inconsistencies and to incorporate several new thermospheric model features.

Justus, C. G.↗
NASA NTRS2007

A New Look at Stratospheric Sudden Warmings. Part II: Evaluation of Numerical Model Simulations

The simulation of major midwinter stratospheric sudden warmings (SSWs) in six stratosphere-resolving general circulation models (GCMs) is examined. The GCMs are compared to a new climatology of SSWs, based on the dynamical characteristics of the events. First, the number, type, and temporal distribution of SSW events are evaluated. Most of the models show a lower frequency of SSW events than the climatology, which has a mean frequency of 6.0 SSWs per decade. Statistical tests show that three of the six models produce significantly fewer SSWs than the climatology, between 1.0 and 2.6 SSWs per decade. Second, four process-based diagnostics are calculated for all of the SSW events in each model. It is found that SSWs in the GCMs compare favorably with dynamical benchmarks for SSW established in the first part of the study. These results indicate that GCMs are capable of quite accurately simulating the dynamics required to produce SSWs, but with lower frequency than the climatology. Further dynamical diagnostics hint that, in at least one case, this is due to a lack of meridional heat flux in the lower stratosphere. Even though the SSWs simulated by most GCMs are dynamically realistic when compared to the NCEP-NCAR reanalysis, the reasons for the relative paucity of SSWs in GCMs remains an important and open question.

Charlton, Andrew J.↗
NASA NTRS2009

Evaluation of Low Altitude Cloud Amount Using CALIPSO Observations

Clouds strongly modulate the transfer of solar and thermal radiation within the atmosphere. Changes in the distribution and properties of clouds in a changing climate represent climate feedbacks affecting climate sensitivity, but the predicted responses of clouds from current climate models are inconsistent. Of particular concern is the response of low clouds, and it is recognized that proper representation of low clouds in global models is in need of improvement. Accurate satellite cloud climatologies are necessary to evaluate model performance, but existing climatologies require validation and are inadequate in some respects for model evaluation. CALIPSO and CloudSat have been acquiring high vertical resolution cloud profiles since launch in 2006. These global cloud datasets represent a new tool for evaluation of both models and existing satellite climatologies. While a recently available merged CALIPSO-CloudSat cloud climatology provides the most comprehensive view of global 3-D cloud distribution, the CALIPSO dataset set on its own has several unique characteristics, including profiles of the atmospheric boundary layer with 30 meter vertical resolution. In this paper we investigate the use of CALIPSO data to characterize boundary layer clouds, looking toward the development of a reliable global dataset suitable for evaluation of model performance.

Winker, Dave↗
NASA NTRS2010

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.

Barrett, Joe H., III↗
NASA NTRS2012

P69 Using the NASA-Unified WRF to Assess the Impacts of Real-Time Vegetation on Simulations of Severe Weather

Since June 2010, the NASA Short-term Prediction Research and Transition (SPoRT; Goodman et al. 2004; Darden et al. 2010; Stano et al. 2012; Fuell et al. 2012) Center has been generating a real-time Normalized Difference Vegetation Index (NDVI) and corresponding Green Vegetation Fraction (GVF) composite based on reflectances from NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. This dataset is generated at 0.01 resolution across the Continental United States (CONUS), and updated daily. The goal of producing such a vegetation dataset is to improve over the default climatological GVF dataset in land surface and numerical weather prediction models, in order to have better simulations of heat and moisture exchange between the land surface and the planetary boundary layer. Details on the SPoRT/MODIS vegetation composite algorithm are presented in Case et al. (2011). Vegetation indices such as GVF and Leaf Area Index (LAI) are used by land surface models (LSMs) to represent the horizontal and vertical density of plant vegetation (Gutman and Ignatov 1998), in order to calculate transpiration, interception and radiative shading. Both of these indices are related to the NDVI; however, there is an inherent ambiguity in determining GVF and LAI simultaneously from NDVI, as described in Gutman and Ignatov (1998). One practice is to specify the LAI while allowing the GVF to vary both spatially and temporally, as is done in the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). Operational versions of Noah within several of the National Centers for Environmental Prediction (NCEP) global and regional modeling systems hold the LAI fixed, while the GVF varies according to a global monthly climatology. This GVF climatology was derived from NDVI data on the NOAA Advanced Very High Resolution Radiometer (AVHRR) polar orbiting satellite, using information from 1985 to 1991 (Gutman and Ignatov 1998; Jiang et al. 2010). Representing data at the mid-point of every month, the climatological dataset is on a grid with 0.144 (~16 km) spatial resolution and is distributed with the community WRF model (Ek et al. 2003; Jiang et al. 2010; Skamarock et al. 2008).

Case, Jonathan L.↗
NASA NTRS2011

Large-Scale Surveys of Snow Depth on Arctic Sea Ice from Operation IceBridge

We show the first results of a large ]scale survey of snow depth on Arctic sea ice from NASA fs Operation IceBridge snow radar system for the 2009 season and compare the data to climatological snow depth values established over the 1954.1991 time period. For multiyear ice, the mean radar derived snow depth is 33.1 cm and the corresponding mean climatological snow depth is 33.4 cm. The small mean difference suggests consistency between contemporary estimates of snow depth with the historical climatology for the multiyear ice region of the Arctic. A 16.5 cm mean difference (climatology minus radar) is observed for first year ice areas suggesting that the increasingly seasonal sea ice cover of the Arctic Ocean has led to an overall loss of snow as the region has transitioned away from a dominantly multiyear ice cover.

Kurtz, Nathan T.↗