Outline of This Brief

1
(No Transcript)
2
Smart Climatology for ASW
Outline of This Brief Slides Topic
3-8 Overview 9-14 Background Definitions and
Concepts 15-19 Data and Methods Initial
Assessments 20-23 a. Atmospheric
Variables 24-35 b. Ocean
Temperature 35-42 c. Ocean
Salinity 43-47 d. Sea Surface Heights
and Currents 48-54 Preliminary
Findings 55-60 Recommendations and
Proposals 61 Contact Information 62-81 Back-Up
Slides
ASW Smart Climo, Aug 07, murphree_at_nps.edu
3
(No Transcript)
4
(No Transcript)
5
(No Transcript)
6
(No Transcript)
7
(No Transcript)
8
(No Transcript)
9
Smart Climatology for ASW
Slides 9-14 Background Definitions and Concepts
ASW Smart Climo, Aug 07, murphree_at_nps.edu
10
10
11
11
12
12
13
13
14
14
15
(No Transcript)
16
(No Transcript)
17
(No Transcript)
18
(No Transcript)
19
(No Transcript)
20
(No Transcript)
21
(No Transcript)
22
(No Transcript)
23
LTM Outgoing Longwave Radiation (OLR), August,
From Reanalysis
OLR is a very useful proxy indicator of clouds
and associated winds, surface heat fluxes, and
precipitation (e.g., low insolation and high
surface winds associated with deep tropical
convection), and thus has implications for
estimating surface forcing of the ocean and
potential impacts on SLD and other ASW-relevant
oceanic variables. Blue (red) indicates deep
atmospheric convection, high precipitation (clear
skies, low precipitation). Low (high) OLR
indicates longwave radiation from relatively cold
(warm) surface. In tropics, lowest OLR values
indicate deep convection, with low amounts of
longwave radiation from high cold cloud tops
while highest values indicate clear sky
conditions and longwave radiation from relatively
warm surfaces (e.g., sea surface). OLR not
available from SMGC or GMCA.
23
From NCEP atmospheric reanalysis
ASW Smart Climo, Aug 07, murphree_at_nps.edu
24
(No Transcript)
25
(No Transcript)
26
(No Transcript)
27
(No Transcript)
28
(No Transcript)
29
(No Transcript)
30
(No Transcript)
31
(No Transcript)
32
(No Transcript)
33
LTM Temperature Profiles, August, From Reanalyses
ASW Smart Climo, Aug 07, murphree_at_nps.edu
33
From SODA oceanic reanalysis
34
Note Near surface temperatures observed during
VSO7 were, in general, 0.5oC warmer than the
long term mean reanalysis temperatures, and
0.5-1.0oC warmer than the GDEM temperatures.
35
(No Transcript)
36
(No Transcript)
37
(No Transcript)
38
(No Transcript)
39
(No Transcript)
40
(No Transcript)
41
LTM Salinity Profiles, August, From Reanalyses
ASW Smart Climo, Aug 07, murphree_at_nps.edu
41
From SODA oceanic reanalysis
42
(No Transcript)
43
(No Transcript)
44
(No Transcript)
45
(No Transcript)
46
(No Transcript)
47
(No Transcript)
48
(No Transcript)
49
Preliminary Findings Smart Climatology and VS07

• Overall T and S patterns in oceanic climatologies
  based on existing civilian reanalyses are similar
  to those in Navy climatologies.
• But there are some surprisingly large differences
  in near-surface T magnitudes (GDEM cooler) that
  may be due to efforts during development of GDEM
  to accentuate mixed layer (e.g., avoid rounded
  off upper ocean T profiles).
• GDEM has considerable small scale structure
  (e.g., bulls eyes, patchy patterns) that may be
  an artifact of the statistical processes used to
  fill in data gaps.
• Some Navy marine atmospheric climatologies
  provide very poor representations of well known
  features of the lower tropospheric circulation
  (e.g., monsoon trough) that are important in
  atmospheric forcing of upper ocean.
• Overall accuracy of climatologies based on
  existing civilian reanalyses appears to be equal
  to or greater than that of Navy climatologies.
• A complete comparative assessment is difficult
  because Navy climatologies do not provide a
  number of important variables that are available
  in reanalyses (e.g., SSH, currents,
  precipitation, estimates of deep convection).

See notes section of this slide for more
details.
ASW Smart Climo, Aug 07, murphree_at_nps.edu
50
(No Transcript)
51
(No Transcript)
52
(No Transcript)
53
(No Transcript)
54
(No Transcript)
55
(No Transcript)
56
Recommended Future Directions for ASW Smart
Climatology

• Develop smart climatology data access, analysis,
  and visualization system for use in ASW RBC and
  other METOC support centers.
• Apply smart climatology methods to improve ASW
  METOC analyses and forecasts, including
• climatological versions of Tier 1-3 products
• climatology based improvements in existing Tier
  1-3 products
• Conduct more in-depth and quantitative
  comparisons of civilian reanalysis data sets with
  Navy atmospheric, oceanic, and acoustic
  climatologies. Assess potential of reanalyses
  and other smart climatology data and methods to
  improve Navy climatologies.
• Use operational analysis and modeling to evaluate
  ability of smart climatology to improve
  operational ASW outcomes.
• Develop online learning center on smart
  climatology and its Navy applications.
• Create a smart climatology steering committee to
  help develop a coordinated and collaborative
  approach for improving military climatology.

The next three slides summarize six proposed
projects based on these recommended directions.
ASW Smart Climo, Aug 07, murphree_at_nps.edu
57
(No Transcript)
58
(No Transcript)
59
(No Transcript)
60
ASW Smart Climatology Project Proposal Summaries
ASW Smart Climo, Aug 07, murphree_at_nps.edu
61
(No Transcript)
62
(No Transcript)
63
(No Transcript)
64
oC
65
(No Transcript)
66
NPS Smart Climatology Research Development
Climatological Environmental Assessment and
Performance Surfaces

• Methods and Results
• Used smart climatology data and methods to
  improve long term mean climatologies of
  evaporation duct heights (EDH) and radar
  propagation in the Indian Ocean and nearby seas.
• Analyzed impacts of seasonal changes climate
  variations (e.g., ENLN, IOZM) on EDH surface
  radar propagation.
• Results (a) new smart EDH climatology with
  substantial improvements over existing Navy
  climatology (b) identified major spatial and
  temporal changes in EDH, including those caused
  by climate variations (c) determined which
  factors EDH and surface radar propagation are
  most sensitive to for different regions and
  seasons (d) found potential for forecasting EDH
  and surface radar propagation at weekly to
  monthly lead times.
• Products (a) smart climatological environmental
  assessment surfaces for EDH and EDH factors and
  (b) smart climatological performance surfaces for
  surface radar propagation (range, CoF) both for
  varying climate scenarios.
• The methods used in this work are directly
  applicable to developing smart climatologies for
  other regions, and for other EM and acoustic
  propagation phenomena.

From NPS thesis research by Lt Katherine Twigg,
Royal Navy, 2007
ASW Smart Climo, Aug 07, murphree_at_nps.edu
67
NPS Smart Climatology - Research Development
Upper Ocean Currents, Nov-Mar, Long Term Mean
(LTM)
Note LTM poleward coastal currents along east
Asia. Results based on 47-year global ocean
reanalysis.
From Ford and Murphree (2007)
ASW Smart Climo, Aug 07, murphree_at_nps.edu
68
(No Transcript)
69
(No Transcript)
70
(No Transcript)
71
(No Transcript)
72
(No Transcript)
73
(No Transcript)
74
(No Transcript)
75
NPS Smart Climatology Prototype Operational
Products
Impacts on Military Operations, Straits of
Taiwan, October
Green favorable for indicated operations /
mission Yellow marginal for indicated operation
/ mission Conditions slightly improved for NE
Taiwan due to decreased monsoonal flow.
ASW Smart Climo, Aug 07, murphree_at_nps.edu
76
(No Transcript)
77
(No Transcript)
78
(No Transcript)
79
(No Transcript)
80
(No Transcript)
81
(No Transcript)