I NAVAL POSTGRADUATE SCHOOL Monterey, California THESIS RESPONSE OF AN ATMOSPHERIC PREDICTION MODEL TO TIME- DEPENDENT SEA-SURFACE TEMPERATURES by Peter Henry Ranelli March 19 84 Thesis Advisor: R. L. Elsberry Approved for public release; distribution unlimited, 121567k SECURITY CLASSIFICATION OF THIS PACE (Whan Data Entarad) REPORT DOCUMENTATION PAGE READ INSTRUCTIONS BEFORE COMPLETING FORM 1. REPORT NUMBER 2. GOVT ACCESSION NO. 3. RECIPIENT'S CATALOG NUMBER 4. TITLE (and Submit) Response of an Atmospheric Prediction Model to Time- Dependent Sea-Surface Temperatures 5. TYPE OF REPORT & PERIOD COVERED Master's Thesis; March, 19 84 6. PERFORMING ORG. REPORT NUMBER 7. AUTHORS Peter Henry Ranelli 8. CONTRACT OR GRANT NUMSERC*,) • ■ PERFORMING ORGANIZATION NAME AND ADDRESS Naval Postgraduate School Monterey, California 9 3943 tO. PROGRAM ELEMENT. PROJECT, TASK AREA 4 WORK UNIT NUMBERS II. CONTROLLING OFFICE NAME ANO ADDRESS Naval Postgraduate School Monterey, California 93943 12. REPORT DATE March 19 84 '3. NUMBER OF PAGES 112 M. MONITORING AGENCY NAME « AOORESSfU dlftarant from Controlling OHIca) IS. SECURITY CLASS, (ol thta report) Unclassified 15*. DECLASSIFICATION/ DOWNGRADING SCHEDULE '» DISTRIBUTION STATEMENT (ot thla Raport) Approved for public release; distribution unlimited. 17. DISTRIBUTION STATEMENT (ot tha abatract antarad In Block 20. II dlltarant trom Raport) IB. SUPPLEMENTARY NOTES U KEY WOROS (Conllnuo on ra Eh CD T3 C • T3 CO P CO 5-1 -H 5h •H i Eh >iCH 03 H fO (d CO rH O 4-> -H CO 5-1 W TJ £ ■H 0 J G 0 m OQ -H 5-1 T3 s CO > -C u -p -p d) CO ■p Q) TJ T3 CD -P c c i id (d a; (13 o M •H G iTJ T3 id O. G cu •H 6 S 5-1 rH a) 0 (d jG -p c -p CO < CO G •H •H • G - H O O ^ CN CO O • vd in o CM CT\ a\ rH 00 CTi CT> CN a\ x: • • • x: • • • x: • • • • • • x: • • • 00 m co r*» VD O U)fO "Sf o o o x: o o o CN rH ^ VD co i CO 1 CO 1 rH rH CN 1 1 1 VD 1 1 rH rH CN 1 1 ■^ rH • • IX) 00 rH .. 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The results were surprising in that there was little change between the two model runs . Specifically, the following conclusions were reached: (1) The total heat flux was strongly dependent on the solar radiation. This was indicated by the small differences between the mean total heat flux for the two model runs and the large standard deviations. (2) The differences in the latent and the sensible heat fluxes were also small but could be attributed to the change in SST. (3) NOGAPS has the ability to generate and maintain a cyclone beyond five days forecast time. This is an important ability for a numerical model to possess before the forecast period can be extended into the 10-15 day range. (4) Differences between the SST run and the control run were small compared to the standard deviation of the changes. Thus, the statistical significance and any improvement in forecast skill cannot be determined with confidence . (5) In general, the control run forecast central pressure and position of storms was better than the SST run. The life cycles of individual storms were better fore- cast by the SST run. This study was limited in its application and results. The initial conditions were chosen to be able to determine the changes in the atmospheric model during the spring transition in the ocean. A decrease in the SST was expected in the wake of a cyclone which would have decreased the amount of cyclogenesis in the SST run. However, a general increase 54 in SST was observed and this hypothesis could not be tested. Additionally, SST fields were only available for the first 7.25 days of the model run. This may have reduced the size of the differences between the two model runs. It is recognized that one case study is not a statistically significant sample. Additional case studies following the same approach need to be conducted before the full impact of a time-dependent SST on an atmospheric model can be deter- mined. These studies should be expanded to include the following: (1) A model with higher vertical resolution should be used. Specifically, the nine-level version of NOGAPS which is now available will serve to improve the propagation of the effects of the changing surface heat fluxes into the atmosphere. (2) The analysis should be extended to include additional model variables and geographical areas. Cloud patterns, precipitation and other diabatic effects need to be examined to determine their impact on the model response. Additionally, model changes in the equatorial and tropical regions will need to be examined. (3) The role of fluxes across the air-sea interface requires additional study with the goal of improving the parameterization of the fluxes. It is felt that the small changes in fluxes in this study resulted in part from limitations in the parameterization method. 55 APPENDIX A THE NAVY OPERATIONAL GLOBAL ATMOSPHERIC PREDICTION SYSTEM The Navy Operational Global Atmospheric Prediction System (NOGAPS) used at the Fleet Numerical Oceanography Center (FNOC) is a slightly modified version of the UCLA general circulation model. NOGAPS has been the Navy's operational atmospheric forecast model since August 19 82. The following sections describe the various features of NOGAPS as used during the experiment. The complete model has been des- cribed by Rosmond (19 81) . A. DYNAMICS The dynamics of the UCLA GCM are described in detail by Arakawa and Lamb (19 77) and are only discussed briefly here. NOGAPS is a primitive equation model. The prognostic varia- bles are horizontal velocity, V, temperature, T, surface pressure, p , and specific humidity, q. Additional prognostic variables associated with the planetary boundary layer (PBL) will be described below. The finite difference scheme used has a spatial resolution of 2.4° lat by 3.0° long. The variables are staggered in the horizontal according to Arakawa scheme C (Fig. 38) . The center grid point contains the T value. The meridional wind component, v, is carried at points north and south of the center point andthe zonal wind com- ponent, u, is carried at points east and west of the center 56 point. The numerical differencing scheme is both energy and enstrophy conserving. NOGAPS uses a sigma coordinate system in the vertical defined as: a = (p - p±)/i\ where : p. = 50 mb and tt = p - p. , i ^s *1 ' p is pressure and p is surface pressure. There are six model layers in the vertical with the top of the model atmos- phere at 50 mb . All prognostic variables except vertical velocity, a, are carried at the middle of each layer. Vertical velocity is carried at the layer interfaces (Fig. 39) . NOGAPS uses a second order (leapfrog) time difference scheme with a four minute time step. Model diabatics are executed every forty minutes. A Matsuno time step is used every fifth time step. This is used to control the compu- tational mode and to assist in the assimilation of the diabatic effects. In regions above 60° latitude, a special Fourier filter is used to avoid an extremely short time step. Whereas a simple three point filter is used equatorward of 60 deg. This filtering reduces the amplitudes of the zonal mass flux and pressure gradients and maintains computational stability. 57 B. MODEL DIABATICS The sophisticated model diabatics contained in NOGAPS is an important component in this experiment. This treatment of the diabatic processes is necessary to adequately simulate fluxes across the air-sea interface and to propagate the full effect of these changes throughout the atmosphere. NOGAPS directly computes the physical processes for: dry convective adjustment large scale precipitation diagnosis of stratus cloud depth mid-level convection ground hydrology surface friction horizontal diffusion of momentum radiative transfer processes cumulus convection 1 . Planetary Boundary Layer The planetary boundary layer (PBL) is defined as a well mixed layer in moisture, moist static energy and momen- tum. It is assumed to be capped by discontinuities in tempera- ture, moisture and momentum. The PBL treatment in this model follows Deardorff (19 72) and has been formulated for the UCLA GCM by Randall (19 76) . It allows for interaction between the PBL and cumulus cloud ensembles and/or a stratus cloud layer at each grid point. Surface fluxes are determined using a bulk Richardson number based on the values of the sea surface temperature and the values of V, T and q from the adiabatic portion of the model. These values are then used to predict a new PBL depth and thre strength of the inversion jumps. 58 The NOGAPS PBL is constrained to remain in the bottom sigma level of the model. This differs from the original formulation of the UCLA GCM, in which the PBL was allowed to pass out of this layer. An overly deep PBL can result in serious computational problems with the model . Constraining the PBL this way imposes a maximum depth of about 20 0 mb on the PBL. 2 . Cumulus Parameterization Cumulus parameterization in NOGAPS follows the scheme of Arakawa-Schubert (19 74) as introduced into the model by Lord (19 7 8) . In the model, cumulus clouds must have their bases at the top of the PBL. Cloud tops can be at all sigma levels above the PBL. Cumulus clouds are modeled as entrain- ing plumes in which environmental air is mixed with the PBL air from which the cloud originated. Tendencies of moisture, temperature and momentum are diagnosed as well as the cloud mass flux. The cloud base mass flux removes mass from the PBL, which decreases the PBL depth. Condensation occurs at each grid point where the air becomes supersaturated. A moist convective adjustment procedure removes convective instability between mid-tropospheric layers that is not eliminated by clouds originating from the PBL. 3. Radiation The radiation parameterization follows Katayama (19 72) and Schlesinger (1976) . It includes both a diurnal variation and interaction with the cloud distribution. Radiative 59 transfer processes for incoming solar radiation are computed. Effects of water vapor, Rayleigh scattering by air molecules and absorption and scattering by water droplets in clouds are included. Reflection due to clouds is also calculated. The model cloud cover predicted by the PBL, the cumulus parameteri- zation and large scale precipitation interact with the long wave radiation. The net surface heat flux is computed as a function of the incoming solar heat flux, long wave radiation and sensible heat flux. In the present model, this affects only the surface temperature over bare land and ice and has no effect on sea-surface temperature. 60 APPENDIX B THERMAL OCEAN PREDICTION SYSTEM-EXPANDED OCEAN THERMAL STRUCTURE (TOPS-EOTS) The Navy began using the TOPS-EOTS system as the opera- tional ocean thermal analysis and forecast system in March 19 83. The objective analysis component is a modified version of the conventional EOTS analysis for the northern hemisphere. The forecast component, TOPS, is a synoptic mixed layer model . The Expanded Ocean Thermal Structure (EOTS) [Mendenhall , et al . , 1978; Holl, et al . , 1979J has been the Navy's opera- tional ocean thermal analysis system for the past several years. It is used to objectively analyze the approximately 200 XBT and 20 00 surface ship observations reported to FNOC in real time each day [Clancy, 19 81J. With some modification, the conventional EOTS analysis has become the objective analy- sis component of TOPS-EOTS. The analysis is performed on the FNOC 63x63 hemispheric polar stereographic grid. The EOTS analysis is performed for the Northern Hemisphere only. Due to the small number of available subsurface temperature profiles, a sea-surface temperature analysis only is performed in the Southern Hemisphere. The EOTS analysis is carried out using a Fields by Infor- mation Blending (FIB) methodology [Holl and Mendenhall, 19 71J . This falls into the broad category of objective analysis 61 known as successive corrections. Twenty-six ocean parameters are analyzed in the upper 400 m on the vertical grid shown in Fig. 40. Parameter one is the primary layer depth (PLD) , which is approximately the depth of the seasonal thermocline. The remaining parameters are temperatures and vertical tempera- ture derivatives. Parameters 2-8 are analyzed at floating levels defined relative to the PLD and parameters 9-26 are associated with fixed levels. The first guess field is the previous 24 hour TOPS fore- cast. The first guess field is horizontally blended, with the observations available for each of the eighteen fixed levels parameters. The analysis is performed over a three- cycle assimilation using reevaluated weights at each grid point during each cycle. The floating parameters are analyzed in the same manner, but with the added complication of deter- mining the PBL. Next, a vertical blending process is per- formed. Vertical blending minimizes inconsistencies in the vertical in a weighted least squares sense. This is completed in one step as opposed to the three-cycle analysis used in the horizontal blending. The sea surface temperature is given an extremely high weight, which effectively anchors the upper part of the thermal profile to this field. The forecast component, which is designated as the Thermal Ocean Prediction System (TOPS) [Clancy and Martin, 1979; Clancy, et al., 1981], is a synoptic mixed layer model that employs the Mellor and Yamada (19 74) Level-2 turbulence 62 parameterization scheme. It includes advection by instan- taneous wind drift and climatological geostrophic currents. The horizontal grid used is the FNOC 63x63 Northern Hemis- phere polar stereographic grid. The values of the mean temperature, T, mean salinity, S, and mean north-south and east-west currents, v and u, are carried at each grid point. Additionally, the advection currents u and v are carried at J a a grid points displaced one-half grid length in the x- and y- directions. The vertical grid includes 18 levels between the surface and 500 m. The variables T, S, u, v, u and v are a a carried at each level. The vertical eddy fluxes and vertical advection velocity, w , are carried at the mid-levels. a The initial conditions for the temperature fields are provided from the EOTS analyses. An initialization algorithm is used to match the EOTS analysis to the vertical levels used in TOPS. Salinity is determined by interpolation of monthly climatology. Wind velocity, surface heat flux (sensi- ble heat, infrared radiation and latent heat) are provided from NOGAPS every six hours and are linearly interpolated to each time step of the model run. 63 APPENDIX C THE SYSTEMATIC ERROR IDENTIFICATION SYSTEM (SEIS) SEIS is a tool to objectively analyze numerical model predictions and produce error statistics for use by opera- tional forecasters. It is presently being implemented for operational use with NOGAPS by the Navy Environmental Predic- tion Research Facility (NEPRF) . The system has been described by Harr et al., (1982) . SEIS operates in a quasi-Lagrangian frame with the reference center located at the center of the storm. The primary algorithm within SEIS is the vortex tracking program (VTP) after Williamson (19 81) . The purpose of VTP is to track synoptic-scale features and produce a listing of operationally relevant parameters following the feature. This program allows each vortex to be examined individually and followed in time. The parameters chosen include ampli- tude (A), ellipticity (£), radius (R) , orientation Cot). , and position of the feature. Amplitude is the magnitude of the vortex central pressure relative to the zonal mean pressure. Ellipticity is a measure of the deviation of the shape of the storm from circular. It is computed as the square of the ratio of the semi-major and semi -minor axes. Orientation is the angle between the x-axis and the semi-major axis, measured counterclockwise from the positive x-axis. Position is 64 specified as either the model grid position or the geographi- cal position. The first step in the VTP is to extract the atmospheric low pressure systems from the sea level pressure fields. After removing the zonal mean pressure, a series of ellipses is fit to the vortices to determine if the low pressure sys- tems are generating, dying, merging or splitting. The original SLP field has now been reduced to a set of parameters des- cribing the ellipses which define the low pressure systems in terms of A, R, e, a and position. Each low pressure system is assigned a unique name. After all maps during a forecast interval have been com- pleted, the fitted parameters are transformed to raw verifi- cation data and raw error statistics. The raw error statistics are differences between the forecast and verifying analysis values of a system's parameters, A, R, e, a and position. Additional derived errors are produced as shown in Fig. 41. Forecast error is the distance between forecast and verifying positions. Track error is the shortest distance between the forecast position and the track position. Timing error is the hourly difference between the verifying position and the position on the verifying track closest to the forecast posi- tion. Speed error is the difference between the distance traveled by the forecast and verifying centers divided by the time increment. Heading error is the angle between the forecast and verifying positions measured from the analysis position. 65 Some modifications were made to SEIS for the purposes of this study. The VTP analysis was extended from the normal 48 h period to 10 days for the longer model forecasts produced in this study. Storms generated during this period required special fitting with the analysis. Also, SEIS was originally designed to compare the forecast field with a verifying field. It was adapted to compare either of the two model runs to the analyzed fields, or one model run to a second run, or all three possibilities at the same time. 66 APPENDIX D FIGURES (a) SST ANALYSIS ATMOSPHERIC MODEL INITIALIZATION TO 10 DAYS ATMOSPHERIC FORECAST (b) OCEAN MODEL SST FORECAST OR HISTORY FILE OF SST (PERFECT PROG) 1 i ' ' TO 10 DAYS IN ATMOSPHERIC MODEL UTILIZATION ATM OSPHERIC FORECAST (c) ATMOSPHERIC FORECAST INITIALIZATION T OCEANIC FORECAST TO 10 DAYS TO 10 DAYS Figure 1. Methods for coupling atmospheric and oceanic models (a) minimal feedback, (b). non- synchronous and (c) synchronous. 67 (a) INITIAL CONDITIONS \ r TO 240 HOI 1 r . , ,1 f i r ' 1 U 1 1 HISTORY FILES (b) INITIAL CONDITIONS SST HISTORY PILES LRST INPUT RT 162 HOURS TO 240 r ' r i * i » f 1 r i r 1 HOL .. ! r 1 f < r i F < f 1 r 1 ! ' r ' ' 1 i i ' f HISTORY FILES Figure 2. Schematic of e xperiment design for (a) control run (b) SST run. SST fields input every twelve hours. History files output every six hours. 68 Figure 3. Sea surface-temperature fields used in the initial conditions in the model run. Contour interval is 2°C. 69 120* E 130* E MO* E 150* E 160* E 170' E 180% 170% 160* » 150* M HO* N 130* M 320* H 35 x 75 H 65 H 55* k 4S* H 35* M 25* » 15* « 5* H Figure 4 . The difference between the SST field input at six hours of the model run and the initial SST field. Contour interval is 0.5°C. Thin solid lines are higher SST, thick solid is no change and the dashed lines are lower temperature. 70 |20*C130*C 110* E ISO* E :6C' E 1 70 ' E I80* m I70' h Ififl" M ISO* M MO* U :ZZ* M J20* M Figure 5. As in Fig. 4, except for 30 h 71 120* C 130* C HO'C ISO* C 160* E 170* E 180* * 170* H 163* k ISC* H 110* N 130* M 120* * 35* N 75* W 55* H 55* H «' W 35' N 25* H 15* W 5*W Figure 6. As in Fig. 4, except for 54 h 72 :20* c 133* e mo': isc' : ieo' : :7c' c ibo' h i7c* k ieo'* iso' ^ ho* * iso" n ;2c'h 35' M 75-' M 55'w 55* N ' r in' r ,..«» «■ .««• 120 E 130 C I«'C ISD'C IM'E 170* C :«•»* 170' - 160'- !30*M 140* M 133* M 120' M (a) Figure 18. (b) As in Fig. 14, except for the latent heat flux. (a) Contour interval is 3 gm-cal/cm^-h (b) Contour interval is 1 gm-cal/cm^-h . 84 (a) 85* M 75' k 65'- 55' M 15* k 35' H 25 ' m IS* * S' M Figure 19 . (b) As in Fig. 14, except for the latent heat flux in the Atlantic Ocean. (a) Contour interval is 3 gm-cal/cm^-h . (b) Contour interval is 1 am-cal/cm^-h . 85 ;•■?*•;• •! >..•,'., :20' £ !30' £ 110* C ISO' C I6C* E 170* C :8C' h 170* * 160* H 150* N HO* M 130* W 12C' M (a) 120* ciao'c no* c iso' c ia* e ito* c iao' * i70* h ire* h jm* k i«'« isd'h 120* n Figure 20. (b) As in Fig. 12, except for the total heat flux (a) Contour interval is 5 gni-cal/cm2-h . (b) Contour interval is 1 gm-cal/cm2-h . 86 8S' H 75* H 55* »< 55* M 15* H 35* N 2S* ■ 15* .4 S'w (a) 85'N TS'm 65' M 55* M «* M 35* M 25* M 15* H 5* M (b) Figure 21. As in Fig. 12, except for the total heat flux in the Atlantic Ocean. (a) Contour interval is 5 gm-cal/cm2-h. (b) Contour interval is 1 gm-cal/cm^-h . 87 L20* £ 130" E MC* C I5C* C 16C* E WO* C ISC* M 170* h 160% 150* K MO* N 130* w 120" K (a) 12:' z :3c* «: ho* c :s:' c :e:'c :?:' c :93* n :tz* * :60* * :so* h no' a J30* h J20* m (b) Figure 22. As in Fig. 14, except for the total heat flux (a) Contour interval is 5 gm-cal/cm2-h. (b) Contour interval is 0.5 gm-cal/cm2-h . 85* N 75* H 55% 55* k 45* M 35' N 25* h 1S% la I 85* N 75' M 65* M 55% 15*- 35% 25% J5' M 5% (b) Figure 23. As in Fig. 14, except for the total heat flux in the Atlantic Ocean. (a) Contour interval is 5 gm-cal/cm^-h . (b) Contour interval is 1 gm-cal/cm^-h . 89 115° E 125° E ' 145° E Figure 24. Tracks for storm P4 . Solid is analysis, dashed is control run and dotted is SST run. "x" indicates position at 126 h. "o" indicates position at 138 h. 90 o (M U">_ LO Y\ * 78 102 126 150 174 198 222 «— 1 • o- i o .— • i "T" i i i i 1 78 102 126 150 174 198 222 (a) (b) LD •H O LO ■ OJ % \ \ vi • / O < '• / a •— ' ^ — u o m- , Cv / \.' / a I. u. - * ■ , ■ / / o - ' LO •• s ' ~~ ** «* mm * f - - O LD oj i i r i 78 102 125 150 174 198 222 (O a) i i i i i i r 78 102 126 150 174 198 222 (d) Figure 25. Storm parameters for the storm P4 . (a) SST at storm center in °C. (b) Heat flux at storm center in gm-cal/cm2-h . (c) Radius of storm in km. (d) Central pressure in mb . Solid is analysis, dashed is control run and dotted is SST run. 91 85° W 75° W 65° W 55° W 45° W 35° W Figure 26. As in Fig. 24 except for storm A2 . "x" indicates position at 126 h. "o" indicates position at 162 h. 92 i r 102 126 150 174 198 222 102 125 150 174 198 222 o o U-) ~* o LO CM / S. '.'*./ \ £«N •' • / O \ k ^^\v y... ■■■: O \ / f .' \X ~ / o \/ >-■' x ' s ' ,— ' V ,.-* * \ , /•' \ ' *' \l o V LO- • — •*' CN. > O ea- rn o LD <\J 1 1 1 1 1 o CM O o 102 126 150 174 198 222 en i i i i r 102 126 150 174 198 222 Figure 27. As in Fig. 25 except for storm A2 93 in in LO CO 45 W 35 W 25" W 15° W Figure 28. As in Fig. 24 except for indicates position at 138 h. storm A3. "x" 94 250 500 750 1000 1250 1500 980 990 1000 1010 1020 J ! L GJ CD E i 10 15 2 OJ- co : • / ; / ; / . 1 cn- 1 1 1 1 1 1 1 i— » '-. Figure 29. As in Fig. 25 except for storm A3 95 zz. _ . c— v.\. . n '.. /A o CO 1 ^ o a O ■ — ■ r V* \ i \x ; * \ v, 1 ' T" — 170° W 160° W 150° W 140° W 130 W Figure 30. As in Fig. 24 except for storm Pi indicates position at 6 h . 'x' 96 CM m_ / o_ LP "™T"" 30 (a) o CNJ o o a o o ^-1 ~ ^ r% ?-. *'--nn • o co- co s s s o CD CO 1 30 (d) 54 Figure 31. As in Fig. 25 except for storm Pi 97 170° E 180° W Figure 32. As in Fig. 24 except for storm P2 98 — . * in- X X X s s / o- 1/7 i o «— • 1 1 1 l 66 90 (b) 114 138 a CM LJ " ' o o" *— 1 o o % •* j» >"" " - o cn- cn o on U) — i— i 138 42 66 90 (d) 114 138 Figure 33. As in Fig. 25 except for storm P2 . 99 40° E Figure 34. As in Fig. 24 except for storm Al 100 LO - O-. LO I 54 78 (a) 102 (b) 126 Figure 35. As in Fig. 25 except for storm Al. 101 in rs. in U3 in K J ^c . ?* / \ Si : o\a rr v " \ - T 1 > _j j m T V 180°1W3° WL60° W50° W140° W3Q° W Figure 36 OAs in Fig. 24 except for storm P3 indicates position at 90 h. 'x' 102 LO- \ \ \ \ o- \ V "**- i V o ■— • i "T" i T~ 162 66 90 114 (b) 138 162 162 Figure 37. As in Fig. 25 except for storm P3 103 2.4 Figure 38. Horizontal distribution ^of model large-scale prognostic variables. a which is not shown is carried at T points. [Sandgathe, 19 81] 104 _ _ 7TCT = 0 50 100 200 5 400 cr CO CO LlI cr a. 600 800 1000 V, T, q 7TCT \V,T, q 7TCT W,T, q 7TCT W, T, q TTCT W, T, q 7TCT \y,L a TOT = 0 Figure 39 . Vertical distribution of model large-scale prognostic variables. Pressure values of sigma levels vary with surface pressure. A surface pressure of 1000 mb is assumed in this figure. [Sandgathe, 19 81] 105 T 0 © 25 ® 50 © 75 100 © 125 to UJ UJ 150 175 200 ® Q_ UJ Q 225 250 ; 275 — 300 — © 325 — 350 — 375 — 400 — © I figure 40. FIXED LEVELS T' ® ® ® (22) @ FLOATING LEVELS T T' V PLD-25 i— PLD© PLD + 12.5 PLD+25 PLD+50 — © ~ ® © © © © © The twenty-six TOPS-EOTS ocean thermal struc- ture parameters. Labeled T, T ' and T" are temperature, first vertical temperature difference and second vertical temperature difference, respectively. Parameters 2-8 are associated with floating levels defined rela- tive to parameter 1, Primary Layer Depth (PLD) Parameters 9-26 are associated with fixed levels. [Clancy and Pollack, 19 82] 106 Figure 41. SEIS derived location errors measures [Harr, et al . , 1983] 107 LIST OF REFERENCES Arakawa, A., and V. Lamb, 1977: Computational design of the basic dynamical processes of the UCLA general circulation model. Methods in Computational Physics, 17, 173-265, Academic Press, New York. Arakawa, A., and W.H. Schubert, 19 74: Interaction of a cumulus cloud ensemble with the large scale environment, Part I. J. Atmos . Sci., 31, 674-701. Arpe, K.V. , 1981: Impact of sea-surface temperature anomaly on medium-range weather forecasts. Unpublished report, European Centre for Medium-Range Weather Forecasts, 8 pages plus 14 figures. Camp, N.T., and R.L. Elsberry, 1978: Oceanic thermal response to strong atmospheric forcing II. The role of one-dimensional processes. J. Phys . Oceanogr., £, 215-224 Clancy, R.M., and P.J. Martin, 19 79: The NORDA/FLENUMOCEANCEN thermodynamical ocean prediction system (TOPS) : A techni- cal description. NORDA Tech. Note 54, NORDA, NSTL Station MS, 28 pp. Clancy, R.M. , 1981: The Expanded Ocean Thermal Structure (EOTS) Analysis: Description, critique and outlook. Paper presented at Ocean Prediction Workshop. 29 April- 2 May, 19 81, Monterey, CA. Clancy, R.M., P.J. Martin, S.A. Piacsek and K.D. Pollak, 19 81: Test and evaluation of an operationally capable synoptic upper ocean forecast system. NORDA Tech. Note 92, NORDA NSTL Station, MS., 67 pp. Clancy, R.M. , and K.D. Pollack, 1983: A real time Synoptic ocean thermal analysis/forecast system. Prog. Oceanogr. , 12_, 383-424. Deardorff, J.W., 19 72: Parameterization of the planetary boundary layer for use in general circulation models. Mon. Wea. Rev., 100, 93-106. Elsberry, R.L., and N.T. Camp, 1978: Oceanic thermal response to strong atmospheric forcing. Part I. Characteristics of forcing events. J. Phys. Oceanogr., 8_, 206-214. 108 Elsberry, R.L., and S.D. Raney , 19 78: Sea-surface tempera- ture response to variations in atmospheric wind forcing. J. Phys . Oceanogr., 8^ 881-887. Elsberry, R.L. , R.L. Haney, R.T. Williams, R.S. Bogart, H.D. Hamilton and E.F. Hinson, 19 82: Ocean/troposphere/ stratosphere forecast systems: a state-of-the-art review. Technical Report CR 8204, Systems and Applied Sciences Corporation, 5 70 Casanova Ave., Monterey, CA., 79 pp. Harr, P. A., T.L. Tsui and L.R. Brody, 1983: Model verifica- tion statistics tailored for the field forecaster. Preprint volume, Seventh Conference on Numerical Weather Prediction, Omaha, NE . , published by the American Meteorological Society, Boston, MA., 241-246. Holl, M.M., and B.R. Mendenhall, 19 71: Fields by information blending, sea level pressure version. Tech. Rept. M167, Meteorology International Inc. , 2600 Garden Road, Suite 145, Monterey CA. , 71 pp. Holl, M.M., M.J. Cumming and B.R. Mendenhall, 19 79: The expanded ocean thermal structure analysis system: A development based on the fields by information blending methodology. Tech. Rept. M241, Meteorology International Inc., 2600 Garden Road, Suite 145, Monterey, CA. , 216 pp. Katayama, A., 1972: A simplified scheme for computing radia- tive transfer in the troposphere. Technical Report No. 6, Dept. of Meteorology, UCLA. Lord, S.J., 1978: Development and observational verification of a cumulus cloud parameterization. Ph.D. Thesis, Dept. of Atmos. Sci., UCLA. Mellor, G.L., and T. Yamada, 1974: A hierarchy of turbulence closure models for planetary boundary layers. J. Atmos. Sci. , 31, 1791-1806. Mendenhall, B.R., M.J. Cumming and M.M. Holl, 1978: The expanded ocean thermal structure analysis system user's manual. Tech. Rept. M2 32, Meteorology International Inc., 2600 Garden Road, Suite 145, Monterey, CA. , 71 pp. Randall, D.A., 1976: The interaction of the planetary boundary layer with large scale circulations. Ph.D. Thesis, Dept. of Atmos. Sci., UCLA. Rosmond, T.E., 1981: NOGAPS : Navy operational global atmos- pheric prediction system. Preprint volume, Fifth Con- ference on Numerical Weather Prediction, Monterey, CA. , published by the American Meteorological Society, Boston, MA., 74 79. 109 Rosmond, T.E., A.L. Weinstein and S.A. Piacsek, 1983: Coupled ocean-atmosphere modeling for 3-15 day numerical prediction: A workshop report. NEPRF Tech. Rept. TR 8305, NEPRF, Monterey, CA. , 81 p. Sanders, F. , and J.R. Gyakum, 19 80: Synoptic dynamic climatology of the "bomb." Mon. Wea. Rev. , 108, 1589-1606 Sandgathe, S .A. , 19 81: A numerical study of the role of air-sea fluxes in extratropical cyclogenesis . Ph.D. Thesis, Dept . of Meteorology, Naval Postgraduate School, Monterey, CA. Schlesinger, M.E., 1976: A numerical simulation of the general circulation of the atmospheric ozone. Ph.D. Thesis, Dept. of Atmos. Sci., UCLA. Williamson, D.L., 19 81: Storm track representation and verification. Tellus, 33, 513-530. 110 INITIAL DISTRIBUTION LIST No. Copies 1. Defense Technical Information Center 2 Cameron Station Alexandria, VA 22 314 2. Library, Code 0142 2 Naval Postgraduate School Monterey, CA 9 3943 3. Chairman, (Code 63Rd) 1 Department of Meteorology Naval Postgraduate School Monterey, CA 93943 4. Chairman, (Code 6 8Mr) 1 Department of Oceanography Naval Postgraduate School Monterey, CA 9 3943 5 . Director 1 Naval Oceanography Division Naval Observatory 34th and Massachusetts Avenue, NW Washington, D.C. 20 390 6 . Commander 1 Naval Oceanography Command NSTL Station Bay St. Louis, MS 39522 7. Commanding Officer 1 Naval Oceanographic Office NSTL Station Bay St. Louis, MS 39522 8. Commanding Officer 1 Fleet Numerical Oceanography Center Monterey, CA 9 3943 9 . Commanding Officer 1 Naval Ocean Research and Development Activity NSTL Station Bay St. Louis, MS 39522 111 10 . Commanding Officer Naval Environmental Prediction Research Facility- Monterey, CA 9 3943 11. Chairman, Oceanography Department U.S. Naval Academy Annapolis, MD 21402 12. Chief of Naval Research 800 N. Quincy Street Arlington, VA 22217 13. Professor R.L. Elsberry, Code 6 3Es Department of Meteorology Naval Postgraduate School Monterey, CA 9 3943 14. C.S. Liou, Code 63Lu Department of Meteorology Naval Postgraduate School Monterey, CA 9 3943 15. LCDR P.H. Ranelli USS New Jersey (BB-62) Fleet Post Office San Francisco, CA 96688 16. LCDR S.A. Sandgathe Joint Typhoon Warning Center CONN AVMARI ANAS Box 12 Fleet Post Office San Francisco, CA 9 6630 17. LT. P.J. Rovero, Code 6 3 Department of Meteorology Naval Postgraduate School Monterey, CA 9 39 43 18. Capt. A.R. Shaffer, Code 63 Department of Meteorology Naval Postgraduate School Monterey, CA 9 3943 19. Dr. T.E. Rosmond Naval Environmental Prediction Research Facility Monterey, CA 9 3943 20. Dr. T.L. Tsui Naval Environmental Prediction Research Facility Monterey, CA 9 39 43 112 a 51 Thesis R2124 cl Ranelli Response of an atmos- pheric prediction model to time-dependent sea- surface temperatures 207551 Thesis R212U cl Ranelli Response of an atmos- pheric prediction model to time- dependent sea- surface temperatures.