| CPC G01N 33/1886 (2013.01) [G01C 13/00 (2013.01)] | 2 Claims |
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1. A marine transportation platform guarantee-oriented analysis and prediction method for a three-dimensional temperature and salinity field, comprising:
(1) based on multi-source marine environmental data, analyzing spatiotemporal distribution characteristics of marine dynamic environmental elements, and studying characteristics of a temperature-salinity relation;
(2) based on analysis of the spatiotemporal characteristics and the study of the characteristics of the temperature-salinity relation, establishing a statistical prediction model of marine environmental dynamic elements by a spatiotemporal empirical orthogonal function method;
(3) based on observation data of sea surface temperature (SST) and salinity obtained by a marine transportation platform, correcting a marine environment forecast field around the marine transportation platform to improve prediction accuracy of a marine environment around the marine transportation platform; and
(4) navigating the marine transportation platform based on the marine environment forecast field;
wherein correcting the marine environment forecast field comprises:
obtaining the marine environment background field according to available data by:
a) when a shore-based marine numerical prediction product transmitted by a shore-based security department is available, loading the shore-based marine numerical prediction product into a marine environment database of the marine transportation platform before sailing, and, using the shore-based marine numerical prediction product as the background field, assimilating real-time/quasi-real-time multi-source marine observation data of the marine transportation platform by using a multi-scale marine data assimilation method to form a real-time analysis field of the marine environment around the marine transportation platform;
b) when the shore-based numerical prediction product is not available, downloading real-time/quasi-real-time satellite remote sensing sea surface temperature and satellite altimeter data, loading the real-time/quasi-real-time satellite remote sensing sea surface temperature and satellite altimeter data into a marine environment data platform of the marine transportation platform before sailing, inverting underwater temperature and salinity data based on a real-time analysis system of the marine transportation platform, thereby obtaining the three-dimensional temperature and salinity field, and using the three-dimensional temperature and salinity field as an initial field for inertial prediction;
c) when the marine transportation platform has been sailing for more than 15 days, and loading the shore-based prediction product fails, based on a reanalysis or statistical prediction product, inverting underwater temperature and salinity data based on the real-time analysis system of the marine transportation platform, and making a real-time analysis product of the marine environment field around the underwater vehicle;
wherein inversion of the three-dimensional temperature and salinity field comprises:
a) construction of a static temperature climate field by:
taking a temperature climatic state analysis product as an initial guess field, historical temperature profile observation data that has undergone processing and quality control is assimilated by using an interpolation data assimilation technique to form static temperature climate field products at different water depths and each of a plurality of horizontal grid points;
temperature observation data Tj,ko at a position j is formed by an interpolation method into climatological temperature data Ti,kc at each grid point position i, at the k-th layer in depth:
 where Ti,kB is the climatic background field;
weight coefficient wi,j in equation (1) is solved by equation (2):
CiWi=Fi (2)
where Wi,j (j=1, . . . , N) is an element of matrix Wi, and Cm,n is an element of matrix Ci, which is equal to a sum of error covariance Cm,nf8 of an initial guess temperature and covariance cm,no of observation errors rm and rn at different observation positions;
b) construction of a static salinity climate field by:
using historical observation data of temperature and salinity profiles for different regions, grids, and different time periods, an empirical regression model of inversion of salinity from temperature is established by using a regression analysis method:
Si,k(T)=Si,k+ai,kS1(T−Ti,k) (3)
where
 where bi,j is a local correlation function:
bi,j=exp{−[(xi−xj)/Lx]2−[(yi−yj)/Ly]2−[(ti−tj)/Lt]2} (7)
where x and y are longitudinal and latitudinal positions respectively; t is time; Lx, Ly, and Lt are length and time correlation scales respectively;
the static temperature climate field is substituted into a temperature-salinity correlation model established above to generate static salinity climate field products at different water depths and each horizontal grid point;
c) inversion of a temperature profile from the SST by:
on the basis of analysis of historical temperature observation data, an empirical regression model for the inversion of the temperature profile from SST is established:
Ti,k(SST)=Ti,k+ai,kT1(sst−Ti,1) (8)
where Ti,k(SST) is the temperature value at grid point i and depth k inverted from the sea surface temperature, Ti,k is the average temperature, SST is the sea surface temperature, and ai,kT1 a regression coefficient;
d) inversion of a temperature profile from sea surface height (SSH) by:
on the basis of analysis of historical observation data of temperature and salinity, an empirical regression model for the inversion of the temperature profile from SSH is established:
Ti,k(h)=Ti,k+ai,kT2(h−hi) (9)
where Ti,k(h) is the temperature value at grid point i and depth k inverted from sea surface height, ai,kT2 sear is a regression coefficient, and h and hi are dynamic height anomaly (deviation) and its average value respectively;
the dynamic height anomaly is computed by:
 where v is the specific volume of seawater, v (0,35, p) is the specific volume of seawater when the seawater temperature is 0° C. and the salinity is 35 psu, and H is the water depth;
a temperature profile extension model is established based on an empirical orthogonal function analysis method; for a profile with missing salinity measurement, the salinity profile is obtained from the temperature profile by using the temperature-salinity relation model established above;
a complete temperature profile is obtained by superimposing a synthetic temperature profile Tksyn onto an observed profile with observation not reaching the seabed:
Tk=Tksyn+[Tk maxo−Tk maxsyn]exp[−(zk−zk max)/Lz] (11)
where Lz is a vertical correlation scale, zk>Zkmax;
the synthetic temperature profile Tksyn is computed by fitting the temperature profile observation that does not reach the seabed to the average temperature and superimposing the empirical orthogonal function Ek corresponding to the maximum eigenvalue:
Tj,ksyn=Tj,k+gjek (12)
where gj is the amplitude of the maximum orthogonal function, computed by:
 where weight w is defined as Wk=(Zk−Zk-1)1/4, k=2, . . . , Mj, W1=w2; and
e) joint inversion of a temperature profile from SST and SSH by:
on the basis of analysis of historical observation data of temperature and salinity, an empirical regression model for the inversion of the temperature profile from SST and SSH is established:
Ti,k(sst,h)=Ti,k+ai,kT3(SST−Ti,t)+ai,kT4(h−hi)+ai,kT5[(SST−Ti,1)(h−hi)−hSSTi] (14)
where Ti,k (sst,h) is the temperature value at grid point i and depth k inverted by sea surface temperature and sea surface height anomalies (deviations), and αi,kT3, αi,kT4 and αi,kT5 are regression coefficients;
wherein correcting the marine environment forecast field by assimilation of observation data of the marine transportation platform comprises:
correcting the background field by a multi-grid three-dimensional variational assimilation technique comprising:
J(n)=½X(n)TX(n)+½(H(n)X(n)−Y(n))TO(n)−1(H(n)X(n)−Y(n)) (15)
where
 where n represents the n-th grid, n=1, 2,3, . . . , N, Xb is a model background field (prediction field) vector, Xa is an analysis field vector, Yobs is an observation field vector; O is an observation field error covariance matrix; H is a bilinear interpolation operator from the model grid to the observation point; X is a control variable, which represents the correction vector relative to the model background field vector, Y is the difference between the observation field and the model background field, and
 where coarse grids correspond to long-wave modes, and fine grids correspond to short-wave modes; and since the wavelength or correlation scale is expressed by the thickness of the grid, the background field error covariance matrix degenerates into an identity matrix:
 and further comprising adjusting the salinity using a temperature-salinity relation curve after the temperature and salinity are forecasted, so as to keep the temperature-salinity relation matched to climatic characteristics.
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