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is equal to - h: hr - x2 p* = U fJ2 So we obtain: where - As we have seen, the correlation coefficient can be described by equation (4): p (r) = po e-'Ir0 It is noted that this relationship has been found for the correlation coefficients computed with the series of measured precipitation depths, while p; stands for the correlation between two series of true precipitation. If, analogously to (II.5), we write: hi = h: +Gi, hi = h* +6 J j then it can easily be shown that: * 1 Pij = Pij -(i f j) A2 + 1 If it is assumed that: the "true" correlation function, p;, will reach the value 1 for r -+ O.*) Thus, combining equations (4) and (II.13), we find: and 1 Po =- A2 + 1 1 If A50.25, then -- x 1 -A2, so A2 o2 + 1 *) assuming that PO is indcpcndent of rij. 1 -po
46 Substituting (11.15) and (11.16) into (11.1 1) we get: I N N I ~2 = o2 [(1 + lim - Z e r i j i ~ N+- ~2 i=l i=l - i - e-rj~ir~ + 1 (11.1 7) With the aid of a computer, the term was determined for a square area S (see Kagan, 1972). In this way is obtained. Next, we consider the situation that n rain gauges are evenly distributed over S. The precipitation depths recorded at these points are denoted by h, , h2, ..., h,. Each rain gauge covers a subarea S/n. The areal precipitation depths of this subarea is now estimated with the precipitation depth recorded at the station belonging to it. That means that: The standard error of estimation for the ith subarea is given by (11.18): Assuming that the errors of estimate for the various subareas are independent, we can write for the standard error of the estimate given by (11.19):
Page 2: C, J, HEMKER Elandsgracht 83 AMSTER
Page 6 and 7: ORIGIN OF PRECIPITATION F.H. SCHMID
Page 8 and 9: CONTENTS I. Origin of precipitation
Page 10 and 11: Now another difficulty remains in t
Page 12 and 13: Generally, another process is of mu
Page 14 and 15: em3 for the 7 flights, while the nu
Page 16 and 17: air penetrates the Netherlands from
Page 18 and 19: THE ACCURACY OF MEASURING AREAL PRE
Page 20 and 21: AIRFLOW TUNNEL W/S 3.05 mlsec. 10 c
Page 22 and 23: where !E3)i is the average value of
Page 24 and 25: 5. SPATIAL VARIABILITY WITHIN A PRE
Page 26 and 27: Ten days total from 36 stations in
Page 28: Fig. 50-5d. The relative error, Z (
Page 32 and 33: An estimate of hG is now given by:
Page 34 and 35: Within an area of about 1100 km2, r
Page 36 and 37: Comparisons between this type of ra
Page 40: used. The arithmetic means of the r
Page 43 and 44: APPENDIX I ERRORS DUE TO THE "RANDO
Page 45: We can write: In fact: A measure of
Page 49 and 50: Radar (see Fig. 1) essentially cons
Page 51 and 52: where Z: Dt is the sum of the 6th p
Page 53 and 54: 6. SOME NUMERICAL EXAMPLES In order
Page 55 and 56: Marshall and Palmer (1 948) gave an
Page 57 and 58: An example of variation in precipit
Page 59 and 60: jii, = mean received power Pt = tra
Page 61 and 62: Fig. 4: Attenuation of radar energy
Page 63 and 64: Fig. 5: Radar and rain gauge result
Page 65 and 66: Fg. 6: Comparison of corrected (rig
Page 67 and 68: isohyets =radar -measurement [mml *
Page 69 and 70: Fig. 11: Precipitation amount. for
Page 71 and 72: MEASUREMENT OF RAINFALL BY RADAR IN
Page 73 and 74: adar-derived pattern of rainfall wa
Page 75 and 76: The accuracy of radar estimates of
Page 77 and 78: provided by polar co-ordinates at c
Page 79 and 80: 6. ECONOMIC CONSIDERATIONS During t
Page 81 and 82: 80 IS WEATHER RADAR RELEVANT TO DUT
Page 83 and 84: Piecing these bits of information t
Page 85 and 86: No. 6. No. 7. No. 8. No. 9. No. 10.
Page 87 and 88: COMMITTEE FOR HYDROLOGICAL RESEARCH
Page 89: No. 17. Proceeding of Technical Mee

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